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<title>Notebook</title>

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textarea{height:auto}
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.control-group.success .input-prepend .add-on,.control-group.success .input-append .add-on{color:#468847;background-color:#dff0d8;border-color:#468847}
.control-group.info .control-label,.control-group.info .help-block,.control-group.info .help-inline{color:#3a87ad}
.control-group.info .checkbox,.control-group.info .radio,.control-group.info input,.control-group.info select,.control-group.info textarea{color:#3a87ad}
.control-group.info input,.control-group.info select,.control-group.info textarea{border-color:#3a87ad;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);box-shadow:inset 0 1px 1px rgba(0,0,0,0.075)}.control-group.info input:focus,.control-group.info select:focus,.control-group.info textarea:focus{border-color:#2d6987;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #7ab5d3;-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #7ab5d3;box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #7ab5d3}
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input:focus:invalid,textarea:focus:invalid,select:focus:invalid{color:#b94a48;border-color:#ee5f5b}input:focus:invalid:focus,textarea:focus:invalid:focus,select:focus:invalid:focus{border-color:#e9322d;-webkit-box-shadow:0 0 6px #f8b9b7;-moz-box-shadow:0 0 6px #f8b9b7;box-shadow:0 0 6px #f8b9b7}
.form-actions{padding:19px 20px 20px;margin-top:20px;margin-bottom:20px;background-color:#f5f5f5;border-top:1px solid #e5e5e5;*zoom:1}.form-actions:before,.form-actions:after{display:table;content:"";line-height:0}
.form-actions:after{clear:both}
.help-block,.help-inline{color:#262626}
.help-block{display:block;margin-bottom:10px}
.help-inline{display:inline-block;*display:inline;*zoom:1;vertical-align:middle;padding-left:5px}
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.input-append .add-on,.input-prepend .add-on{display:inline-block;width:auto;height:20px;min-width:16px;padding:4px 5px;font-size:13px;font-weight:normal;line-height:20px;text-align:center;text-shadow:0 1px 0 #fff;background-color:#eee;border:1px solid #ccc}
.input-append .add-on,.input-prepend .add-on,.input-append .btn,.input-prepend .btn,.input-append .btn-group>.dropdown-toggle,.input-prepend .btn-group>.dropdown-toggle{vertical-align:top;border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}
.input-append .active,.input-prepend .active{background-color:#a9dba9;border-color:#46a546}
.input-prepend .add-on,.input-prepend .btn{margin-right:-1px}
.input-prepend .add-on:first-child,.input-prepend .btn:first-child{border-radius:4px 0 0 4px;-webkit-border-radius:4px 0 0 4px;-moz-border-radius:4px 0 0 4px;border-radius:4px 0 0 4px}
.input-append input,.input-append select,.input-append .uneditable-input{border-radius:4px 0 0 4px;-webkit-border-radius:4px 0 0 4px;-moz-border-radius:4px 0 0 4px;border-radius:4px 0 0 4px}.input-append input+.btn-group .btn:last-child,.input-append select+.btn-group .btn:last-child,.input-append .uneditable-input+.btn-group .btn:last-child{border-radius:0 4px 4px 0;-webkit-border-radius:0 4px 4px 0;-moz-border-radius:0 4px 4px 0;border-radius:0 4px 4px 0}
.input-append .add-on,.input-append .btn,.input-append .btn-group{margin-left:-1px}
.input-append .add-on:last-child,.input-append .btn:last-child,.input-append .btn-group:last-child>.dropdown-toggle{border-radius:0 4px 4px 0;-webkit-border-radius:0 4px 4px 0;-moz-border-radius:0 4px 4px 0;border-radius:0 4px 4px 0}
.input-prepend.input-append input,.input-prepend.input-append select,.input-prepend.input-append .uneditable-input{border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}.input-prepend.input-append input+.btn-group .btn,.input-prepend.input-append select+.btn-group .btn,.input-prepend.input-append .uneditable-input+.btn-group .btn{border-radius:0 4px 4px 0;-webkit-border-radius:0 4px 4px 0;-moz-border-radius:0 4px 4px 0;border-radius:0 4px 4px 0}
.input-prepend.input-append .add-on:first-child,.input-prepend.input-append .btn:first-child{margin-right:-1px;border-radius:4px 0 0 4px;-webkit-border-radius:4px 0 0 4px;-moz-border-radius:4px 0 0 4px;border-radius:4px 0 0 4px}
.input-prepend.input-append .add-on:last-child,.input-prepend.input-append .btn:last-child{margin-left:-1px;border-radius:0 4px 4px 0;-webkit-border-radius:0 4px 4px 0;-moz-border-radius:0 4px 4px 0;border-radius:0 4px 4px 0}
.input-prepend.input-append .btn-group:first-child{margin-left:0}
input.search-query{padding-right:14px;padding-right:4px \9;padding-left:14px;padding-left:4px \9;margin-bottom:0;border-radius:15px;-webkit-border-radius:15px;-moz-border-radius:15px;border-radius:15px}
.form-search .input-append .search-query,.form-search .input-prepend .search-query{border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}
.form-search .input-append .search-query{border-radius:14px 0 0 14px;-webkit-border-radius:14px 0 0 14px;-moz-border-radius:14px 0 0 14px;border-radius:14px 0 0 14px}
.form-search .input-append .btn{border-radius:0 14px 14px 0;-webkit-border-radius:0 14px 14px 0;-moz-border-radius:0 14px 14px 0;border-radius:0 14px 14px 0}
.form-search .input-prepend .search-query{border-radius:0 14px 14px 0;-webkit-border-radius:0 14px 14px 0;-moz-border-radius:0 14px 14px 0;border-radius:0 14px 14px 0}
.form-search .input-prepend .btn{border-radius:14px 0 0 14px;-webkit-border-radius:14px 0 0 14px;-moz-border-radius:14px 0 0 14px;border-radius:14px 0 0 14px}
.form-search input,.form-inline input,.form-horizontal input,.form-search textarea,.form-inline textarea,.form-horizontal textarea,.form-search select,.form-inline select,.form-horizontal select,.form-search .help-inline,.form-inline .help-inline,.form-horizontal .help-inline,.form-search .uneditable-input,.form-inline .uneditable-input,.form-horizontal .uneditable-input,.form-search .input-prepend,.form-inline .input-prepend,.form-horizontal .input-prepend,.form-search .input-append,.form-inline .input-append,.form-horizontal .input-append{display:inline-block;*display:inline;*zoom:1;margin-bottom:0;vertical-align:middle}
.form-search .hide,.form-inline .hide,.form-horizontal .hide{display:none}
.form-search label,.form-inline label,.form-search .btn-group,.form-inline .btn-group{display:inline-block}
.form-search .input-append,.form-inline .input-append,.form-search .input-prepend,.form-inline .input-prepend{margin-bottom:0}
.form-search .radio,.form-search .checkbox,.form-inline .radio,.form-inline .checkbox{padding-left:0;margin-bottom:0;vertical-align:middle}
.form-search .radio input[type="radio"],.form-search .checkbox input[type="checkbox"],.form-inline .radio input[type="radio"],.form-inline .checkbox input[type="checkbox"]{float:left;margin-right:3px;margin-left:0}
.control-group{margin-bottom:10px}
legend+.control-group{margin-top:20px;-webkit-margin-top-collapse:separate}
.form-horizontal .control-group{margin-bottom:20px;*zoom:1}.form-horizontal .control-group:before,.form-horizontal .control-group:after{display:table;content:"";line-height:0}
.form-horizontal .control-group:after{clear:both}
.form-horizontal .control-label{float:left;width:160px;padding-top:5px;text-align:right}
.form-horizontal .controls{*display:inline-block;*padding-left:20px;margin-left:180px;*margin-left:0}.form-horizontal .controls:first-child{*padding-left:180px}
.form-horizontal .help-block{margin-bottom:0}
.form-horizontal input+.help-block,.form-horizontal select+.help-block,.form-horizontal textarea+.help-block,.form-horizontal .uneditable-input+.help-block,.form-horizontal .input-prepend+.help-block,.form-horizontal .input-append+.help-block{margin-top:10px}
.form-horizontal .form-actions{padding-left:180px}
table{max-width:100%;background-color:transparent;border-collapse:collapse;border-spacing:0}
.table{width:100%;margin-bottom:20px}.table th,.table td{padding:8px;line-height:20px;text-align:left;vertical-align:top;border-top:1px solid #ddd}
.table th{font-weight:bold}
.table thead th{vertical-align:bottom}
.table caption+thead tr:first-child th,.table caption+thead tr:first-child td,.table colgroup+thead tr:first-child th,.table colgroup+thead tr:first-child td,.table thead:first-child tr:first-child th,.table thead:first-child tr:first-child td{border-top:0}
.table tbody+tbody{border-top:2px solid #ddd}
.table .table{background-color:#fff}
.table-condensed th,.table-condensed td{padding:4px 5px}
.table-bordered{border:1px solid #ddd;border-collapse:separate;*border-collapse:collapse;border-left:0;border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}.table-bordered th,.table-bordered td{border-left:1px solid #ddd}
.table-bordered caption+thead tr:first-child th,.table-bordered caption+tbody tr:first-child th,.table-bordered caption+tbody tr:first-child td,.table-bordered colgroup+thead tr:first-child th,.table-bordered colgroup+tbody tr:first-child th,.table-bordered colgroup+tbody tr:first-child td,.table-bordered thead:first-child tr:first-child th,.table-bordered tbody:first-child tr:first-child th,.table-bordered tbody:first-child tr:first-child td{border-top:0}
.table-bordered thead:first-child tr:first-child>th:first-child,.table-bordered tbody:first-child tr:first-child>td:first-child,.table-bordered tbody:first-child tr:first-child>th:first-child{-webkit-border-top-left-radius:4px;-moz-border-radius-topleft:4px;border-top-left-radius:4px}
.table-bordered thead:first-child tr:first-child>th:last-child,.table-bordered tbody:first-child tr:first-child>td:last-child,.table-bordered tbody:first-child tr:first-child>th:last-child{-webkit-border-top-right-radius:4px;-moz-border-radius-topright:4px;border-top-right-radius:4px}
.table-bordered thead:last-child tr:last-child>th:first-child,.table-bordered tbody:last-child tr:last-child>td:first-child,.table-bordered tbody:last-child tr:last-child>th:first-child,.table-bordered tfoot:last-child tr:last-child>td:first-child,.table-bordered tfoot:last-child tr:last-child>th:first-child{-webkit-border-bottom-left-radius:4px;-moz-border-radius-bottomleft:4px;border-bottom-left-radius:4px}
.table-bordered thead:last-child tr:last-child>th:last-child,.table-bordered tbody:last-child tr:last-child>td:last-child,.table-bordered tbody:last-child tr:last-child>th:last-child,.table-bordered tfoot:last-child tr:last-child>td:last-child,.table-bordered tfoot:last-child tr:last-child>th:last-child{-webkit-border-bottom-right-radius:4px;-moz-border-radius-bottomright:4px;border-bottom-right-radius:4px}
.table-bordered tfoot+tbody:last-child tr:last-child td:first-child{-webkit-border-bottom-left-radius:0;-moz-border-radius-bottomleft:0;border-bottom-left-radius:0}
.table-bordered tfoot+tbody:last-child tr:last-child td:last-child{-webkit-border-bottom-right-radius:0;-moz-border-radius-bottomright:0;border-bottom-right-radius:0}
.table-bordered caption+thead tr:first-child th:first-child,.table-bordered caption+tbody tr:first-child td:first-child,.table-bordered colgroup+thead tr:first-child th:first-child,.table-bordered colgroup+tbody tr:first-child td:first-child{-webkit-border-top-left-radius:4px;-moz-border-radius-topleft:4px;border-top-left-radius:4px}
.table-bordered caption+thead tr:first-child th:last-child,.table-bordered caption+tbody tr:first-child td:last-child,.table-bordered colgroup+thead tr:first-child th:last-child,.table-bordered colgroup+tbody tr:first-child td:last-child{-webkit-border-top-right-radius:4px;-moz-border-radius-topright:4px;border-top-right-radius:4px}
.table-striped tbody>tr:nth-child(odd)>td,.table-striped tbody>tr:nth-child(odd)>th{background-color:#f9f9f9}
.table-hover tbody tr:hover>td,.table-hover tbody tr:hover>th{background-color:#f5f5f5}
table td[class*="span"],table th[class*="span"],.row-fluid table td[class*="span"],.row-fluid table th[class*="span"]{display:table-cell;float:none;margin-left:0}
.table td.span1,.table th.span1{float:none;width:44px;margin-left:0}
.table td.span2,.table th.span2{float:none;width:124px;margin-left:0}
.table td.span3,.table th.span3{float:none;width:204px;margin-left:0}
.table td.span4,.table th.span4{float:none;width:284px;margin-left:0}
.table td.span5,.table th.span5{float:none;width:364px;margin-left:0}
.table td.span6,.table th.span6{float:none;width:444px;margin-left:0}
.table td.span7,.table th.span7{float:none;width:524px;margin-left:0}
.table td.span8,.table th.span8{float:none;width:604px;margin-left:0}
.table td.span9,.table th.span9{float:none;width:684px;margin-left:0}
.table td.span10,.table th.span10{float:none;width:764px;margin-left:0}
.table td.span11,.table th.span11{float:none;width:844px;margin-left:0}
.table td.span12,.table th.span12{float:none;width:924px;margin-left:0}
.table tbody tr.success>td{background-color:#dff0d8}
.table tbody tr.error>td{background-color:#f2dede}
.table tbody tr.warning>td{background-color:#fcf8e3}
.table tbody tr.info>td{background-color:#d9edf7}
.table-hover tbody tr.success:hover>td{background-color:#d0e9c6}
.table-hover tbody tr.error:hover>td{background-color:#ebcccc}
.table-hover tbody tr.warning:hover>td{background-color:#faf2cc}
.table-hover tbody tr.info:hover>td{background-color:#c4e3f3}
[class^="icon-"],[class*=" icon-"]{display:inline-block;width:14px;height:14px;*margin-right:.3em;line-height:14px;vertical-align:text-top;background-image:url("../img/glyphicons-halflings.png");background-position:14px 14px;background-repeat:no-repeat;margin-top:1px}
.icon-white,.nav-pills>.active>a>[class^="icon-"],.nav-pills>.active>a>[class*=" icon-"],.nav-list>.active>a>[class^="icon-"],.nav-list>.active>a>[class*=" icon-"],.navbar-inverse .nav>.active>a>[class^="icon-"],.navbar-inverse .nav>.active>a>[class*=" icon-"],.dropdown-menu>li>a:hover>[class^="icon-"],.dropdown-menu>li>a:focus>[class^="icon-"],.dropdown-menu>li>a:hover>[class*=" icon-"],.dropdown-menu>li>a:focus>[class*=" icon-"],.dropdown-menu>.active>a>[class^="icon-"],.dropdown-menu>.active>a>[class*=" icon-"],.dropdown-submenu:hover>a>[class^="icon-"],.dropdown-submenu:focus>a>[class^="icon-"],.dropdown-submenu:hover>a>[class*=" icon-"],.dropdown-submenu:focus>a>[class*=" icon-"]{background-image:url("../img/glyphicons-halflings-white.png")}
.icon-glass{background-position:0 0}
.icon-music{background-position:-24px 0}
.icon-search{background-position:-48px 0}
.icon-envelope{background-position:-72px 0}
.icon-heart{background-position:-96px 0}
.icon-star{background-position:-120px 0}
.icon-star-empty{background-position:-144px 0}
.icon-user{background-position:-168px 0}
.icon-film{background-position:-192px 0}
.icon-th-large{background-position:-216px 0}
.icon-th{background-position:-240px 0}
.icon-th-list{background-position:-264px 0}
.icon-ok{background-position:-288px 0}
.icon-remove{background-position:-312px 0}
.icon-zoom-in{background-position:-336px 0}
.icon-zoom-out{background-position:-360px 0}
.icon-off{background-position:-384px 0}
.icon-signal{background-position:-408px 0}
.icon-cog{background-position:-432px 0}
.icon-trash{background-position:-456px 0}
.icon-home{background-position:0 -24px}
.icon-file{background-position:-24px -24px}
.icon-time{background-position:-48px -24px}
.icon-road{background-position:-72px -24px}
.icon-download-alt{background-position:-96px -24px}
.icon-download{background-position:-120px -24px}
.icon-upload{background-position:-144px -24px}
.icon-inbox{background-position:-168px -24px}
.icon-play-circle{background-position:-192px -24px}
.icon-repeat{background-position:-216px -24px}
.icon-refresh{background-position:-240px -24px}
.icon-list-alt{background-position:-264px -24px}
.icon-lock{background-position:-287px -24px}
.icon-flag{background-position:-312px -24px}
.icon-headphones{background-position:-336px -24px}
.icon-volume-off{background-position:-360px -24px}
.icon-volume-down{background-position:-384px -24px}
.icon-volume-up{background-position:-408px -24px}
.icon-qrcode{background-position:-432px -24px}
.icon-barcode{background-position:-456px -24px}
.icon-tag{background-position:0 -48px}
.icon-tags{background-position:-25px -48px}
.icon-book{background-position:-48px -48px}
.icon-bookmark{background-position:-72px -48px}
.icon-print{background-position:-96px -48px}
.icon-camera{background-position:-120px -48px}
.icon-font{background-position:-144px -48px}
.icon-bold{background-position:-167px -48px}
.icon-italic{background-position:-192px -48px}
.icon-text-height{background-position:-216px -48px}
.icon-text-width{background-position:-240px -48px}
.icon-align-left{background-position:-264px -48px}
.icon-align-center{background-position:-288px -48px}
.icon-align-right{background-position:-312px -48px}
.icon-align-justify{background-position:-336px -48px}
.icon-list{background-position:-360px -48px}
.icon-indent-left{background-position:-384px -48px}
.icon-indent-right{background-position:-408px -48px}
.icon-facetime-video{background-position:-432px -48px}
.icon-picture{background-position:-456px -48px}
.icon-pencil{background-position:0 -72px}
.icon-map-marker{background-position:-24px -72px}
.icon-adjust{background-position:-48px -72px}
.icon-tint{background-position:-72px -72px}
.icon-edit{background-position:-96px -72px}
.icon-share{background-position:-120px -72px}
.icon-check{background-position:-144px -72px}
.icon-move{background-position:-168px -72px}
.icon-step-backward{background-position:-192px -72px}
.icon-fast-backward{background-position:-216px -72px}
.icon-backward{background-position:-240px -72px}
.icon-play{background-position:-264px -72px}
.icon-pause{background-position:-288px -72px}
.icon-stop{background-position:-312px -72px}
.icon-forward{background-position:-336px -72px}
.icon-fast-forward{background-position:-360px -72px}
.icon-step-forward{background-position:-384px -72px}
.icon-eject{background-position:-408px -72px}
.icon-chevron-left{background-position:-432px -72px}
.icon-chevron-right{background-position:-456px -72px}
.icon-plus-sign{background-position:0 -96px}
.icon-minus-sign{background-position:-24px -96px}
.icon-remove-sign{background-position:-48px -96px}
.icon-ok-sign{background-position:-72px -96px}
.icon-question-sign{background-position:-96px -96px}
.icon-info-sign{background-position:-120px -96px}
.icon-screenshot{background-position:-144px -96px}
.icon-remove-circle{background-position:-168px -96px}
.icon-ok-circle{background-position:-192px -96px}
.icon-ban-circle{background-position:-216px -96px}
.icon-arrow-left{background-position:-240px -96px}
.icon-arrow-right{background-position:-264px -96px}
.icon-arrow-up{background-position:-289px -96px}
.icon-arrow-down{background-position:-312px -96px}
.icon-share-alt{background-position:-336px -96px}
.icon-resize-full{background-position:-360px -96px}
.icon-resize-small{background-position:-384px -96px}
.icon-plus{background-position:-408px -96px}
.icon-minus{background-position:-433px -96px}
.icon-asterisk{background-position:-456px -96px}
.icon-exclamation-sign{background-position:0 -120px}
.icon-gift{background-position:-24px -120px}
.icon-leaf{background-position:-48px -120px}
.icon-fire{background-position:-72px -120px}
.icon-eye-open{background-position:-96px -120px}
.icon-eye-close{background-position:-120px -120px}
.icon-warning-sign{background-position:-144px -120px}
.icon-plane{background-position:-168px -120px}
.icon-calendar{background-position:-192px -120px}
.icon-random{background-position:-216px -120px;width:16px}
.icon-comment{background-position:-240px -120px}
.icon-magnet{background-position:-264px -120px}
.icon-chevron-up{background-position:-288px -120px}
.icon-chevron-down{background-position:-313px -119px}
.icon-retweet{background-position:-336px -120px}
.icon-shopping-cart{background-position:-360px -120px}
.icon-folder-close{background-position:-384px -120px;width:16px}
.icon-folder-open{background-position:-408px -120px;width:16px}
.icon-resize-vertical{background-position:-432px -119px}
.icon-resize-horizontal{background-position:-456px -118px}
.icon-hdd{background-position:0 -144px}
.icon-bullhorn{background-position:-24px -144px}
.icon-bell{background-position:-48px -144px}
.icon-certificate{background-position:-72px -144px}
.icon-thumbs-up{background-position:-96px -144px}
.icon-thumbs-down{background-position:-120px -144px}
.icon-hand-right{background-position:-144px -144px}
.icon-hand-left{background-position:-168px -144px}
.icon-hand-up{background-position:-192px -144px}
.icon-hand-down{background-position:-216px -144px}
.icon-circle-arrow-right{background-position:-240px -144px}
.icon-circle-arrow-left{background-position:-264px -144px}
.icon-circle-arrow-up{background-position:-288px -144px}
.icon-circle-arrow-down{background-position:-312px -144px}
.icon-globe{background-position:-336px -144px}
.icon-wrench{background-position:-360px -144px}
.icon-tasks{background-position:-384px -144px}
.icon-filter{background-position:-408px -144px}
.icon-briefcase{background-position:-432px -144px}
.icon-fullscreen{background-position:-456px -144px}
.dropup,.dropdown{position:relative}
.dropdown-toggle{*margin-bottom:-3px}
.dropdown-toggle:active,.open .dropdown-toggle{outline:0}
.caret{display:inline-block;width:0;height:0;vertical-align:top;border-top:4px solid #000;border-right:4px solid transparent;border-left:4px solid transparent;content:""}
.dropdown .caret{margin-top:8px;margin-left:2px}
.dropdown-menu{position:absolute;top:100%;left:0;z-index:1000;display:none;float:left;min-width:160px;padding:5px 0;margin:2px 0 0;list-style:none;background-color:#fff;border:1px solid #ccc;border:1px solid rgba(0,0,0,0.2);*border-right-width:2px;*border-bottom-width:2px;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px;-webkit-box-shadow:0 5px 10px rgba(0,0,0,0.2);-moz-box-shadow:0 5px 10px rgba(0,0,0,0.2);box-shadow:0 5px 10px rgba(0,0,0,0.2);-webkit-background-clip:padding-box;-moz-background-clip:padding;background-clip:padding-box}.dropdown-menu.pull-right{right:0;left:auto}
.dropdown-menu .divider{*width:100%;height:1px;margin:9px 1px;*margin:-5px 0 5px;overflow:hidden;background-color:#e5e5e5;border-bottom:1px solid #fff}
.dropdown-menu>li>a{display:block;padding:3px 20px;clear:both;font-weight:normal;line-height:20px;color:#333;white-space:nowrap}
.dropdown-menu>li>a:hover,.dropdown-menu>li>a:focus,.dropdown-submenu:hover>a,.dropdown-submenu:focus>a{text-decoration:none;color:#fff;background-color:#0081c2;background-image:-moz-linear-gradient(top, #08c, #0077b3);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#08c), to(#0077b3));background-image:-webkit-linear-gradient(top, #08c, #0077b3);background-image:-o-linear-gradient(top, #08c, #0077b3);background-image:linear-gradient(to bottom, #08c, #0077b3);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff0088cc', endColorstr='#ff0077b3', GradientType=0)}
.dropdown-menu>.active>a,.dropdown-menu>.active>a:hover,.dropdown-menu>.active>a:focus{color:#fff;text-decoration:none;outline:0;background-color:#0081c2;background-image:-moz-linear-gradient(top, #08c, #0077b3);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#08c), to(#0077b3));background-image:-webkit-linear-gradient(top, #08c, #0077b3);background-image:-o-linear-gradient(top, #08c, #0077b3);background-image:linear-gradient(to bottom, #08c, #0077b3);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff0088cc', endColorstr='#ff0077b3', GradientType=0)}
.dropdown-menu>.disabled>a,.dropdown-menu>.disabled>a:hover,.dropdown-menu>.disabled>a:focus{color:#999}
.dropdown-menu>.disabled>a:hover,.dropdown-menu>.disabled>a:focus{text-decoration:none;background-color:transparent;background-image:none;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false);cursor:default}
.open{*z-index:1000}.open>.dropdown-menu{display:block}
.dropdown-backdrop{position:fixed;left:0;right:0;bottom:0;top:0;z-index:990}
.pull-right>.dropdown-menu{right:0;left:auto}
.dropup .caret,.navbar-fixed-bottom .dropdown .caret{border-top:0;border-bottom:4px solid #000;content:""}
.dropup .dropdown-menu,.navbar-fixed-bottom .dropdown .dropdown-menu{top:auto;bottom:100%;margin-bottom:1px}
.dropdown-submenu{position:relative}
.dropdown-submenu>.dropdown-menu{top:0;left:100%;margin-top:-6px;margin-left:-1px;border-radius:0 6px 6px 6px;-webkit-border-radius:0 6px 6px 6px;-moz-border-radius:0 6px 6px 6px;border-radius:0 6px 6px 6px}
.dropdown-submenu:hover>.dropdown-menu{display:block}
.dropup .dropdown-submenu>.dropdown-menu{top:auto;bottom:0;margin-top:0;margin-bottom:-2px;border-radius:5px 5px 5px 0;-webkit-border-radius:5px 5px 5px 0;-moz-border-radius:5px 5px 5px 0;border-radius:5px 5px 5px 0}
.dropdown-submenu>a:after{display:block;content:" ";float:right;width:0;height:0;border-color:transparent;border-style:solid;border-width:5px 0 5px 5px;border-left-color:#ccc;margin-top:5px;margin-right:-10px}
.dropdown-submenu:hover>a:after{border-left-color:#fff}
.dropdown-submenu.pull-left{float:none}.dropdown-submenu.pull-left>.dropdown-menu{left:-100%;margin-left:10px;border-radius:6px 0 6px 6px;-webkit-border-radius:6px 0 6px 6px;-moz-border-radius:6px 0 6px 6px;border-radius:6px 0 6px 6px}
.dropdown .dropdown-menu .nav-header{padding-left:20px;padding-right:20px}
.typeahead{z-index:1051;margin-top:2px;border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}
.well{min-height:20px;padding:19px;margin-bottom:20px;background-color:#f5f5f5;border:1px solid #e3e3e3;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.05);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.05);box-shadow:inset 0 1px 1px rgba(0,0,0,0.05)}.well blockquote{border-color:#ddd;border-color:rgba(0,0,0,0.15)}
.well-large{padding:24px;border-radius:6px;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px}
.well-small{padding:9px;border-radius:3px;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px}
.fade{opacity:0;-webkit-transition:opacity .15s linear;-moz-transition:opacity .15s linear;-o-transition:opacity .15s linear;transition:opacity .15s linear}.fade.in{opacity:1}
.collapse{position:relative;height:0;overflow:hidden;-webkit-transition:height .35s ease;-moz-transition:height .35s ease;-o-transition:height .35s ease;transition:height .35s ease}.collapse.in{height:auto}
.close{float:right;font-size:20px;font-weight:bold;line-height:20px;color:#000;text-shadow:0 1px 0 #fff;opacity:.2;filter:alpha(opacity=20)}.close:hover,.close:focus{color:#000;text-decoration:none;cursor:pointer;opacity:.4;filter:alpha(opacity=40)}
button.close{padding:0;cursor:pointer;background:transparent;border:0;-webkit-appearance:none}
.btn{display:inline-block;*display:inline;*zoom:1;padding:4px 12px;margin-bottom:0;font-size:13px;line-height:20px;text-align:center;vertical-align:middle;cursor:pointer;color:#333;text-shadow:0 1px 1px rgba(255,255,255,0.75);background-color:#f5f5f5;background-image:-moz-linear-gradient(top, #fff, #e6e6e6);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#fff), to(#e6e6e6));background-image:-webkit-linear-gradient(top, #fff, #e6e6e6);background-image:-o-linear-gradient(top, #fff, #e6e6e6);background-image:linear-gradient(to bottom, #fff, #e6e6e6);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#ffe6e6e6', GradientType=0);border-color:#e6e6e6 #e6e6e6 #bfbfbf;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#e6e6e6;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false);border:1px solid #ccc;*border:0;border-bottom-color:#b3b3b3;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;*margin-left:.3em;-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05);-moz-box-shadow:inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05);box-shadow:inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05)}.btn:hover,.btn:focus,.btn:active,.btn.active,.btn.disabled,.btn[disabled]{color:#333;background-color:#e6e6e6;*background-color:#d9d9d9}
.btn:active,.btn.active{background-color:#ccc \9}
.btn:first-child{*margin-left:0}
.btn:hover,.btn:focus{color:#333;text-decoration:none;background-position:0 -15px;-webkit-transition:background-position .1s linear;-moz-transition:background-position .1s linear;-o-transition:background-position .1s linear;transition:background-position .1s linear}
.btn:focus{outline:thin dotted #333;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}
.btn.active,.btn:active{background-image:none;outline:0;-webkit-box-shadow:inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05);-moz-box-shadow:inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05);box-shadow:inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05)}
.btn.disabled,.btn[disabled]{cursor:default;background-image:none;opacity:.65;filter:alpha(opacity=65);-webkit-box-shadow:none;-moz-box-shadow:none;box-shadow:none}
.btn-large{padding:11px 19px;font-size:16.25px;border-radius:6px;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px}
.btn-large [class^="icon-"],.btn-large [class*=" icon-"]{margin-top:4px}
.btn-small{padding:2px 10px;font-size:11.049999999999999px;border-radius:3px;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px}
.btn-small [class^="icon-"],.btn-small [class*=" icon-"]{margin-top:0}
.btn-mini [class^="icon-"],.btn-mini [class*=" icon-"]{margin-top:-1px}
.btn-mini{padding:0 6px;font-size:9.75px;border-radius:3px;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px}
.btn-block{display:block;width:100%;padding-left:0;padding-right:0;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}
.btn-block+.btn-block{margin-top:5px}
input[type="submit"].btn-block,input[type="reset"].btn-block,input[type="button"].btn-block{width:100%}
.btn-primary.active,.btn-warning.active,.btn-danger.active,.btn-success.active,.btn-info.active,.btn-inverse.active{color:rgba(255,255,255,0.75)}
.btn-primary{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#006dcc;background-image:-moz-linear-gradient(top, #08c, #04c);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#08c), to(#04c));background-image:-webkit-linear-gradient(top, #08c, #04c);background-image:-o-linear-gradient(top, #08c, #04c);background-image:linear-gradient(to bottom, #08c, #04c);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff0088cc', endColorstr='#ff0044cc', GradientType=0);border-color:#04c #04c #002a80;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#04c;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false)}.btn-primary:hover,.btn-primary:focus,.btn-primary:active,.btn-primary.active,.btn-primary.disabled,.btn-primary[disabled]{color:#fff;background-color:#04c;*background-color:#003bb3}
.btn-primary:active,.btn-primary.active{background-color:#039 \9}
.btn-warning{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#faa732;background-image:-moz-linear-gradient(top, #fbb450, #f89406);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#fbb450), to(#f89406));background-image:-webkit-linear-gradient(top, #fbb450, #f89406);background-image:-o-linear-gradient(top, #fbb450, #f89406);background-image:linear-gradient(to bottom, #fbb450, #f89406);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffbb450', endColorstr='#fff89406', GradientType=0);border-color:#f89406 #f89406 #ad6704;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#f89406;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false)}.btn-warning:hover,.btn-warning:focus,.btn-warning:active,.btn-warning.active,.btn-warning.disabled,.btn-warning[disabled]{color:#fff;background-color:#f89406;*background-color:#df8505}
.btn-warning:active,.btn-warning.active{background-color:#c67605 \9}
.btn-danger{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#da4f49;background-image:-moz-linear-gradient(top, #ee5f5b, #bd362f);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#ee5f5b), to(#bd362f));background-image:-webkit-linear-gradient(top, #ee5f5b, #bd362f);background-image:-o-linear-gradient(top, #ee5f5b, #bd362f);background-image:linear-gradient(to bottom, #ee5f5b, #bd362f);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffee5f5b', endColorstr='#ffbd362f', GradientType=0);border-color:#bd362f #bd362f #802420;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#bd362f;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false)}.btn-danger:hover,.btn-danger:focus,.btn-danger:active,.btn-danger.active,.btn-danger.disabled,.btn-danger[disabled]{color:#fff;background-color:#bd362f;*background-color:#a9302a}
.btn-danger:active,.btn-danger.active{background-color:#942a25 \9}
.btn-success{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#5bb75b;background-image:-moz-linear-gradient(top, #62c462, #51a351);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#62c462), to(#51a351));background-image:-webkit-linear-gradient(top, #62c462, #51a351);background-image:-o-linear-gradient(top, #62c462, #51a351);background-image:linear-gradient(to bottom, #62c462, #51a351);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff62c462', endColorstr='#ff51a351', GradientType=0);border-color:#51a351 #51a351 #387038;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#51a351;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false)}.btn-success:hover,.btn-success:focus,.btn-success:active,.btn-success.active,.btn-success.disabled,.btn-success[disabled]{color:#fff;background-color:#51a351;*background-color:#499249}
.btn-success:active,.btn-success.active{background-color:#408140 \9}
.btn-info{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#49afcd;background-image:-moz-linear-gradient(top, #5bc0de, #2f96b4);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#5bc0de), to(#2f96b4));background-image:-webkit-linear-gradient(top, #5bc0de, #2f96b4);background-image:-o-linear-gradient(top, #5bc0de, #2f96b4);background-image:linear-gradient(to bottom, #5bc0de, #2f96b4);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff2f96b4', GradientType=0);border-color:#2f96b4 #2f96b4 #1f6377;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#2f96b4;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false)}.btn-info:hover,.btn-info:focus,.btn-info:active,.btn-info.active,.btn-info.disabled,.btn-info[disabled]{color:#fff;background-color:#2f96b4;*background-color:#2a85a0}
.btn-info:active,.btn-info.active{background-color:#24748c \9}
.btn-inverse{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#363636;background-image:-moz-linear-gradient(top, #444, #222);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#444), to(#222));background-image:-webkit-linear-gradient(top, #444, #222);background-image:-o-linear-gradient(top, #444, #222);background-image:linear-gradient(to bottom, #444, #222);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff444444', endColorstr='#ff222222', GradientType=0);border-color:#222 #222 #000;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#222;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false)}.btn-inverse:hover,.btn-inverse:focus,.btn-inverse:active,.btn-inverse.active,.btn-inverse.disabled,.btn-inverse[disabled]{color:#fff;background-color:#222;*background-color:#151515}
.btn-inverse:active,.btn-inverse.active{background-color:#080808 \9}
button.btn,input[type="submit"].btn{*padding-top:3px;*padding-bottom:3px}button.btn::-moz-focus-inner,input[type="submit"].btn::-moz-focus-inner{padding:0;border:0}
button.btn.btn-large,input[type="submit"].btn.btn-large{*padding-top:7px;*padding-bottom:7px}
button.btn.btn-small,input[type="submit"].btn.btn-small{*padding-top:3px;*padding-bottom:3px}
button.btn.btn-mini,input[type="submit"].btn.btn-mini{*padding-top:1px;*padding-bottom:1px}
.btn-link,.btn-link:active,.btn-link[disabled]{background-color:transparent;background-image:none;-webkit-box-shadow:none;-moz-box-shadow:none;box-shadow:none}
.btn-link{border-color:transparent;cursor:pointer;color:#08c;border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}
.btn-link:hover,.btn-link:focus{color:#005580;text-decoration:underline;background-color:transparent}
.btn-link[disabled]:hover,.btn-link[disabled]:focus{color:#333;text-decoration:none}
.btn-group{position:relative;display:inline-block;*display:inline;*zoom:1;font-size:0;vertical-align:middle;white-space:nowrap;*margin-left:.3em}.btn-group:first-child{*margin-left:0}
.btn-group+.btn-group{margin-left:5px}
.btn-toolbar{font-size:0;margin-top:10px;margin-bottom:10px}.btn-toolbar>.btn+.btn,.btn-toolbar>.btn-group+.btn,.btn-toolbar>.btn+.btn-group{margin-left:5px}
.btn-group>.btn{position:relative;border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}
.btn-group>.btn+.btn{margin-left:-1px}
.btn-group>.btn,.btn-group>.dropdown-menu,.btn-group>.popover{font-size:13px}
.btn-group>.btn-mini{font-size:9.75px}
.btn-group>.btn-small{font-size:11.049999999999999px}
.btn-group>.btn-large{font-size:16.25px}
.btn-group>.btn:first-child{margin-left:0;-webkit-border-top-left-radius:4px;-moz-border-radius-topleft:4px;border-top-left-radius:4px;-webkit-border-bottom-left-radius:4px;-moz-border-radius-bottomleft:4px;border-bottom-left-radius:4px}
.btn-group>.btn:last-child,.btn-group>.dropdown-toggle{-webkit-border-top-right-radius:4px;-moz-border-radius-topright:4px;border-top-right-radius:4px;-webkit-border-bottom-right-radius:4px;-moz-border-radius-bottomright:4px;border-bottom-right-radius:4px}
.btn-group>.btn.large:first-child{margin-left:0;-webkit-border-top-left-radius:6px;-moz-border-radius-topleft:6px;border-top-left-radius:6px;-webkit-border-bottom-left-radius:6px;-moz-border-radius-bottomleft:6px;border-bottom-left-radius:6px}
.btn-group>.btn.large:last-child,.btn-group>.large.dropdown-toggle{-webkit-border-top-right-radius:6px;-moz-border-radius-topright:6px;border-top-right-radius:6px;-webkit-border-bottom-right-radius:6px;-moz-border-radius-bottomright:6px;border-bottom-right-radius:6px}
.btn-group>.btn:hover,.btn-group>.btn:focus,.btn-group>.btn:active,.btn-group>.btn.active{z-index:2}
.btn-group .dropdown-toggle:active,.btn-group.open .dropdown-toggle{outline:0}
.btn-group>.btn+.dropdown-toggle{padding-left:8px;padding-right:8px;-webkit-box-shadow:inset 1px 0 0 rgba(255,255,255,.125), inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05);-moz-box-shadow:inset 1px 0 0 rgba(255,255,255,.125), inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05);box-shadow:inset 1px 0 0 rgba(255,255,255,.125), inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05);*padding-top:5px;*padding-bottom:5px}
.btn-group>.btn-mini+.dropdown-toggle{padding-left:5px;padding-right:5px;*padding-top:2px;*padding-bottom:2px}
.btn-group>.btn-small+.dropdown-toggle{*padding-top:5px;*padding-bottom:4px}
.btn-group>.btn-large+.dropdown-toggle{padding-left:12px;padding-right:12px;*padding-top:7px;*padding-bottom:7px}
.btn-group.open .dropdown-toggle{background-image:none;-webkit-box-shadow:inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05);-moz-box-shadow:inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05);box-shadow:inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05)}
.btn-group.open .btn.dropdown-toggle{background-color:#e6e6e6}
.btn-group.open .btn-primary.dropdown-toggle{background-color:#04c}
.btn-group.open .btn-warning.dropdown-toggle{background-color:#f89406}
.btn-group.open .btn-danger.dropdown-toggle{background-color:#bd362f}
.btn-group.open .btn-success.dropdown-toggle{background-color:#51a351}
.btn-group.open .btn-info.dropdown-toggle{background-color:#2f96b4}
.btn-group.open .btn-inverse.dropdown-toggle{background-color:#222}
.btn .caret{margin-top:8px;margin-left:0}
.btn-large .caret{margin-top:6px}
.btn-large .caret{border-left-width:5px;border-right-width:5px;border-top-width:5px}
.btn-mini .caret,.btn-small .caret{margin-top:8px}
.dropup .btn-large .caret{border-bottom-width:5px}
.btn-primary .caret,.btn-warning .caret,.btn-danger .caret,.btn-info .caret,.btn-success .caret,.btn-inverse .caret{border-top-color:#fff;border-bottom-color:#fff}
.btn-group-vertical{display:inline-block;*display:inline;*zoom:1}
.btn-group-vertical>.btn{display:block;float:none;max-width:100%;border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}
.btn-group-vertical>.btn+.btn{margin-left:0;margin-top:-1px}
.btn-group-vertical>.btn:first-child{border-radius:4px 4px 0 0;-webkit-border-radius:4px 4px 0 0;-moz-border-radius:4px 4px 0 0;border-radius:4px 4px 0 0}
.btn-group-vertical>.btn:last-child{border-radius:0 0 4px 4px;-webkit-border-radius:0 0 4px 4px;-moz-border-radius:0 0 4px 4px;border-radius:0 0 4px 4px}
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.alert{padding:8px 35px 8px 14px;margin-bottom:20px;text-shadow:0 1px 0 rgba(255,255,255,0.5);background-color:#fcf8e3;border:1px solid #fbeed5;border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}
.alert,.alert h4{color:#c09853}
.alert h4{margin:0}
.alert .close{position:relative;top:-2px;right:-21px;line-height:20px}
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.alert-danger h4,.alert-error h4{color:#b94a48}
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.alert-block p+p{margin-top:5px}
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.tabbable:after{clear:both}
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.tabs-below>.nav-tabs>li>a{border-radius:0 0 4px 4px;-webkit-border-radius:0 0 4px 4px;-moz-border-radius:0 0 4px 4px;border-radius:0 0 4px 4px}.tabs-below>.nav-tabs>li>a:hover,.tabs-below>.nav-tabs>li>a:focus{border-bottom-color:transparent;border-top-color:#ddd}
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.btn-navbar .icon-bar+.icon-bar{margin-top:3px}
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.navbar-inverse .btn-navbar:active,.navbar-inverse .btn-navbar.active{background-color:#000 \9}
.breadcrumb{padding:8px 15px;margin:0 0 20px;list-style:none;background-color:#f5f5f5;border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}.breadcrumb>li{display:inline-block;*display:inline;*zoom:1;text-shadow:0 1px 0 #fff}.breadcrumb>li>.divider{padding:0 5px;color:#ccc}
.breadcrumb>.active{color:#999}
.pagination{margin:20px 0}
.pagination ul{display:inline-block;*display:inline;*zoom:1;margin-left:0;margin-bottom:0;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;-webkit-box-shadow:0 1px 2px rgba(0,0,0,0.05);-moz-box-shadow:0 1px 2px rgba(0,0,0,0.05);box-shadow:0 1px 2px rgba(0,0,0,0.05)}
.pagination ul>li{display:inline}
.pagination ul>li>a,.pagination ul>li>span{float:left;padding:4px 12px;line-height:20px;text-decoration:none;background-color:#fff;border:1px solid #ddd;border-left-width:0}
.pagination ul>li>a:hover,.pagination ul>li>a:focus,.pagination ul>.active>a,.pagination ul>.active>span{background-color:#f5f5f5}
.pagination ul>.active>a,.pagination ul>.active>span{color:#999;cursor:default}
.pagination ul>.disabled>span,.pagination ul>.disabled>a,.pagination ul>.disabled>a:hover,.pagination ul>.disabled>a:focus{color:#999;background-color:transparent;cursor:default}
.pagination ul>li:first-child>a,.pagination ul>li:first-child>span{border-left-width:1px;-webkit-border-top-left-radius:4px;-moz-border-radius-topleft:4px;border-top-left-radius:4px;-webkit-border-bottom-left-radius:4px;-moz-border-radius-bottomleft:4px;border-bottom-left-radius:4px}
.pagination ul>li:last-child>a,.pagination ul>li:last-child>span{-webkit-border-top-right-radius:4px;-moz-border-radius-topright:4px;border-top-right-radius:4px;-webkit-border-bottom-right-radius:4px;-moz-border-radius-bottomright:4px;border-bottom-right-radius:4px}
.pagination-centered{text-align:center}
.pagination-right{text-align:right}
.pagination-large ul>li>a,.pagination-large ul>li>span{padding:11px 19px;font-size:16.25px}
.pagination-large ul>li:first-child>a,.pagination-large ul>li:first-child>span{-webkit-border-top-left-radius:6px;-moz-border-radius-topleft:6px;border-top-left-radius:6px;-webkit-border-bottom-left-radius:6px;-moz-border-radius-bottomleft:6px;border-bottom-left-radius:6px}
.pagination-large ul>li:last-child>a,.pagination-large ul>li:last-child>span{-webkit-border-top-right-radius:6px;-moz-border-radius-topright:6px;border-top-right-radius:6px;-webkit-border-bottom-right-radius:6px;-moz-border-radius-bottomright:6px;border-bottom-right-radius:6px}
.pagination-mini ul>li:first-child>a,.pagination-small ul>li:first-child>a,.pagination-mini ul>li:first-child>span,.pagination-small ul>li:first-child>span{-webkit-border-top-left-radius:3px;-moz-border-radius-topleft:3px;border-top-left-radius:3px;-webkit-border-bottom-left-radius:3px;-moz-border-radius-bottomleft:3px;border-bottom-left-radius:3px}
.pagination-mini ul>li:last-child>a,.pagination-small ul>li:last-child>a,.pagination-mini ul>li:last-child>span,.pagination-small ul>li:last-child>span{-webkit-border-top-right-radius:3px;-moz-border-radius-topright:3px;border-top-right-radius:3px;-webkit-border-bottom-right-radius:3px;-moz-border-radius-bottomright:3px;border-bottom-right-radius:3px}
.pagination-small ul>li>a,.pagination-small ul>li>span{padding:2px 10px;font-size:11.049999999999999px}
.pagination-mini ul>li>a,.pagination-mini ul>li>span{padding:0 6px;font-size:9.75px}
.pager{margin:20px 0;list-style:none;text-align:center;*zoom:1}.pager:before,.pager:after{display:table;content:"";line-height:0}
.pager:after{clear:both}
.pager li{display:inline}
.pager li>a,.pager li>span{display:inline-block;padding:5px 14px;background-color:#fff;border:1px solid #ddd;border-radius:15px;-webkit-border-radius:15px;-moz-border-radius:15px;border-radius:15px}
.pager li>a:hover,.pager li>a:focus{text-decoration:none;background-color:#f5f5f5}
.pager .next>a,.pager .next>span{float:right}
.pager .previous>a,.pager .previous>span{float:left}
.pager .disabled>a,.pager .disabled>a:hover,.pager .disabled>a:focus,.pager .disabled>span{color:#999;background-color:#fff;cursor:default}
.modal-backdrop{position:fixed;top:0;right:0;bottom:0;left:0;z-index:1040;background-color:#000}.modal-backdrop.fade{opacity:0}
.modal-backdrop,.modal-backdrop.fade.in{opacity:.8;filter:alpha(opacity=80)}
.modal{position:fixed;top:10%;left:50%;z-index:1050;width:560px;margin-left:-280px;background-color:#fff;border:1px solid #999;border:1px solid rgba(0,0,0,0.3);*border:1px solid #999;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px;-webkit-box-shadow:0 3px 7px rgba(0,0,0,0.3);-moz-box-shadow:0 3px 7px rgba(0,0,0,0.3);box-shadow:0 3px 7px rgba(0,0,0,0.3);-webkit-background-clip:padding-box;-moz-background-clip:padding-box;background-clip:padding-box;outline:none}.modal.fade{-webkit-transition:opacity .3s linear, top .3s ease-out;-moz-transition:opacity .3s linear, top .3s ease-out;-o-transition:opacity .3s linear, top .3s ease-out;transition:opacity .3s linear, top .3s ease-out;top:-25%}
.modal.fade.in{top:10%}
.modal-header{padding:9px 15px;border-bottom:1px solid #eee}.modal-header .close{margin-top:2px}
.modal-header h3{margin:0;line-height:30px}
.modal-body{position:relative;overflow-y:auto;max-height:400px;padding:15px}
.modal-form{margin-bottom:0}
.modal-footer{padding:14px 15px 15px;margin-bottom:0;text-align:right;background-color:#f5f5f5;border-top:1px solid #ddd;-webkit-border-radius:0 0 6px 6px;-moz-border-radius:0 0 6px 6px;border-radius:0 0 6px 6px;-webkit-box-shadow:inset 0 1px 0 #fff;-moz-box-shadow:inset 0 1px 0 #fff;box-shadow:inset 0 1px 0 #fff;*zoom:1}.modal-footer:before,.modal-footer:after{display:table;content:"";line-height:0}
.modal-footer:after{clear:both}
.modal-footer .btn+.btn{margin-left:5px;margin-bottom:0}
.modal-footer .btn-group .btn+.btn{margin-left:-1px}
.modal-footer .btn-block+.btn-block{margin-left:0}
.tooltip{position:absolute;z-index:1030;display:block;visibility:visible;font-size:11px;line-height:1.4;opacity:0;filter:alpha(opacity=0)}.tooltip.in{opacity:.8;filter:alpha(opacity=80)}
.tooltip.top{margin-top:-3px;padding:5px 0}
.tooltip.right{margin-left:3px;padding:0 5px}
.tooltip.bottom{margin-top:3px;padding:5px 0}
.tooltip.left{margin-left:-3px;padding:0 5px}
.tooltip-inner{max-width:200px;padding:8px;color:#fff;text-align:center;text-decoration:none;background-color:#000;border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}
.tooltip-arrow{position:absolute;width:0;height:0;border-color:transparent;border-style:solid}
.tooltip.top .tooltip-arrow{bottom:0;left:50%;margin-left:-5px;border-width:5px 5px 0;border-top-color:#000}
.tooltip.right .tooltip-arrow{top:50%;left:0;margin-top:-5px;border-width:5px 5px 5px 0;border-right-color:#000}
.tooltip.left .tooltip-arrow{top:50%;right:0;margin-top:-5px;border-width:5px 0 5px 5px;border-left-color:#000}
.tooltip.bottom .tooltip-arrow{top:0;left:50%;margin-left:-5px;border-width:0 5px 5px;border-bottom-color:#000}
.popover{position:absolute;top:0;left:0;z-index:1010;display:none;max-width:276px;padding:1px;text-align:left;background-color:#fff;-webkit-background-clip:padding-box;-moz-background-clip:padding;background-clip:padding-box;border:1px solid #ccc;border:1px solid rgba(0,0,0,0.2);-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px;-webkit-box-shadow:0 5px 10px rgba(0,0,0,0.2);-moz-box-shadow:0 5px 10px rgba(0,0,0,0.2);box-shadow:0 5px 10px rgba(0,0,0,0.2);white-space:normal}.popover.top{margin-top:-10px}
.popover.right{margin-left:10px}
.popover.bottom{margin-top:10px}
.popover.left{margin-left:-10px}
.popover-title{margin:0;padding:8px 14px;font-size:14px;font-weight:normal;line-height:18px;background-color:#f7f7f7;border-bottom:1px solid #ebebeb;border-radius:5px 5px 0 0;-webkit-border-radius:5px 5px 0 0;-moz-border-radius:5px 5px 0 0;border-radius:5px 5px 0 0}.popover-title:empty{display:none}
.popover-content{padding:9px 14px}
.popover .arrow,.popover .arrow:after{position:absolute;display:block;width:0;height:0;border-color:transparent;border-style:solid}
.popover .arrow{border-width:11px}
.popover .arrow:after{border-width:10px;content:""}
.popover.top .arrow{left:50%;margin-left:-11px;border-bottom-width:0;border-top-color:#999;border-top-color:rgba(0,0,0,0.25);bottom:-11px}.popover.top .arrow:after{bottom:1px;margin-left:-10px;border-bottom-width:0;border-top-color:#fff}
.popover.right .arrow{top:50%;left:-11px;margin-top:-11px;border-left-width:0;border-right-color:#999;border-right-color:rgba(0,0,0,0.25)}.popover.right .arrow:after{left:1px;bottom:-10px;border-left-width:0;border-right-color:#fff}
.popover.bottom .arrow{left:50%;margin-left:-11px;border-top-width:0;border-bottom-color:#999;border-bottom-color:rgba(0,0,0,0.25);top:-11px}.popover.bottom .arrow:after{top:1px;margin-left:-10px;border-top-width:0;border-bottom-color:#fff}
.popover.left .arrow{top:50%;right:-11px;margin-top:-11px;border-right-width:0;border-left-color:#999;border-left-color:rgba(0,0,0,0.25)}.popover.left .arrow:after{right:1px;border-right-width:0;border-left-color:#fff;bottom:-10px}
.thumbnails{margin-left:-20px;list-style:none;*zoom:1}.thumbnails:before,.thumbnails:after{display:table;content:"";line-height:0}
.thumbnails:after{clear:both}
.row-fluid .thumbnails{margin-left:0}
.thumbnails>li{float:left;margin-bottom:20px;margin-left:20px}
.thumbnail{display:block;padding:4px;line-height:20px;border:1px solid #ddd;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;-webkit-box-shadow:0 1px 3px rgba(0,0,0,0.055);-moz-box-shadow:0 1px 3px rgba(0,0,0,0.055);box-shadow:0 1px 3px rgba(0,0,0,0.055);-webkit-transition:all .2s ease-in-out;-moz-transition:all .2s ease-in-out;-o-transition:all .2s ease-in-out;transition:all .2s ease-in-out}
a.thumbnail:hover,a.thumbnail:focus{border-color:#08c;-webkit-box-shadow:0 1px 4px rgba(0,105,214,0.25);-moz-box-shadow:0 1px 4px rgba(0,105,214,0.25);box-shadow:0 1px 4px rgba(0,105,214,0.25)}
.thumbnail>img{display:block;max-width:100%;margin-left:auto;margin-right:auto}
.thumbnail .caption{padding:9px;color:#555}
.media,.media-body{overflow:hidden;*overflow:visible;zoom:1}
.media,.media .media{margin-top:15px}
.media:first-child{margin-top:0}
.media-object{display:block}
.media-heading{margin:0 0 5px}
.media>.pull-left{margin-right:10px}
.media>.pull-right{margin-left:10px}
.media-list{margin-left:0;list-style:none}
.label,.badge{display:inline-block;padding:2px 4px;font-size:10.998px;font-weight:bold;line-height:14px;color:#fff;vertical-align:baseline;white-space:nowrap;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#999}
.label{border-radius:3px;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px}
.badge{padding-left:9px;padding-right:9px;border-radius:9px;-webkit-border-radius:9px;-moz-border-radius:9px;border-radius:9px}
.label:empty,.badge:empty{display:none}
a.label:hover,a.label:focus,a.badge:hover,a.badge:focus{color:#fff;text-decoration:none;cursor:pointer}
.label-important,.badge-important{background-color:#b94a48}
.label-important[href],.badge-important[href]{background-color:#953b39}
.label-warning,.badge-warning{background-color:#f89406}
.label-warning[href],.badge-warning[href]{background-color:#c67605}
.label-success,.badge-success{background-color:#468847}
.label-success[href],.badge-success[href]{background-color:#356635}
.label-info,.badge-info{background-color:#3a87ad}
.label-info[href],.badge-info[href]{background-color:#2d6987}
.label-inverse,.badge-inverse{background-color:#333}
.label-inverse[href],.badge-inverse[href]{background-color:#1a1a1a}
.btn .label,.btn .badge{position:relative;top:-1px}
.btn-mini .label,.btn-mini .badge{top:0}
@-webkit-keyframes progress-bar-stripes{from{background-position:40px 0} to{background-position:0 0}}@-moz-keyframes progress-bar-stripes{from{background-position:40px 0} to{background-position:0 0}}@-ms-keyframes progress-bar-stripes{from{background-position:40px 0} to{background-position:0 0}}@-o-keyframes progress-bar-stripes{from{background-position:0 0} to{background-position:40px 0}}@keyframes progress-bar-stripes{from{background-position:40px 0} to{background-position:0 0}}.progress{overflow:hidden;height:20px;margin-bottom:20px;background-color:#f7f7f7;background-image:-moz-linear-gradient(top, #f5f5f5, #f9f9f9);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#f5f5f5), to(#f9f9f9));background-image:-webkit-linear-gradient(top, #f5f5f5, #f9f9f9);background-image:-o-linear-gradient(top, #f5f5f5, #f9f9f9);background-image:linear-gradient(to bottom, #f5f5f5, #f9f9f9);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5', endColorstr='#fff9f9f9', GradientType=0);-webkit-box-shadow:inset 0 1px 2px rgba(0,0,0,0.1);-moz-box-shadow:inset 0 1px 2px rgba(0,0,0,0.1);box-shadow:inset 0 1px 2px rgba(0,0,0,0.1);border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}
.progress .bar{width:0;height:100%;color:#fff;float:left;font-size:12px;text-align:center;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#0e90d2;background-image:-moz-linear-gradient(top, #149bdf, #0480be);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#149bdf), to(#0480be));background-image:-webkit-linear-gradient(top, #149bdf, #0480be);background-image:-o-linear-gradient(top, #149bdf, #0480be);background-image:linear-gradient(to bottom, #149bdf, #0480be);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff149bdf', endColorstr='#ff0480be', GradientType=0);-webkit-box-shadow:inset 0 -1px 0 rgba(0,0,0,0.15);-moz-box-shadow:inset 0 -1px 0 rgba(0,0,0,0.15);box-shadow:inset 0 -1px 0 rgba(0,0,0,0.15);-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;-webkit-transition:width .6s ease;-moz-transition:width .6s ease;-o-transition:width .6s ease;transition:width .6s ease}
.progress .bar+.bar{-webkit-box-shadow:inset 1px 0 0 rgba(0,0,0,.15), inset 0 -1px 0 rgba(0,0,0,.15);-moz-box-shadow:inset 1px 0 0 rgba(0,0,0,.15), inset 0 -1px 0 rgba(0,0,0,.15);box-shadow:inset 1px 0 0 rgba(0,0,0,.15), inset 0 -1px 0 rgba(0,0,0,.15)}
.progress-striped .bar{background-color:#149bdf;background-image:-webkit-gradient(linear, 0 100%, 100% 0, color-stop(.25, rgba(255,255,255,0.15)), color-stop(.25, transparent), color-stop(.5, transparent), color-stop(.5, rgba(255,255,255,0.15)), color-stop(.75, rgba(255,255,255,0.15)), color-stop(.75, transparent), to(transparent));background-image:-webkit-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-moz-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-o-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);-webkit-background-size:40px 40px;-moz-background-size:40px 40px;-o-background-size:40px 40px;background-size:40px 40px}
.progress.active .bar{-webkit-animation:progress-bar-stripes 2s linear infinite;-moz-animation:progress-bar-stripes 2s linear infinite;-ms-animation:progress-bar-stripes 2s linear infinite;-o-animation:progress-bar-stripes 2s linear infinite;animation:progress-bar-stripes 2s linear infinite}
.progress-danger .bar,.progress .bar-danger{background-color:#dd514c;background-image:-moz-linear-gradient(top, #ee5f5b, #c43c35);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#ee5f5b), to(#c43c35));background-image:-webkit-linear-gradient(top, #ee5f5b, #c43c35);background-image:-o-linear-gradient(top, #ee5f5b, #c43c35);background-image:linear-gradient(to bottom, #ee5f5b, #c43c35);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffee5f5b', endColorstr='#ffc43c35', GradientType=0)}
.progress-danger.progress-striped .bar,.progress-striped .bar-danger{background-color:#ee5f5b;background-image:-webkit-gradient(linear, 0 100%, 100% 0, color-stop(.25, rgba(255,255,255,0.15)), color-stop(.25, transparent), color-stop(.5, transparent), color-stop(.5, rgba(255,255,255,0.15)), color-stop(.75, rgba(255,255,255,0.15)), color-stop(.75, transparent), to(transparent));background-image:-webkit-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-moz-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-o-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent)}
.progress-success .bar,.progress .bar-success{background-color:#5eb95e;background-image:-moz-linear-gradient(top, #62c462, #57a957);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#62c462), to(#57a957));background-image:-webkit-linear-gradient(top, #62c462, #57a957);background-image:-o-linear-gradient(top, #62c462, #57a957);background-image:linear-gradient(to bottom, #62c462, #57a957);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff62c462', endColorstr='#ff57a957', GradientType=0)}
.progress-success.progress-striped .bar,.progress-striped .bar-success{background-color:#62c462;background-image:-webkit-gradient(linear, 0 100%, 100% 0, color-stop(.25, rgba(255,255,255,0.15)), color-stop(.25, transparent), color-stop(.5, transparent), color-stop(.5, rgba(255,255,255,0.15)), color-stop(.75, rgba(255,255,255,0.15)), color-stop(.75, transparent), to(transparent));background-image:-webkit-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-moz-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-o-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent)}
.progress-info .bar,.progress .bar-info{background-color:#4bb1cf;background-image:-moz-linear-gradient(top, #5bc0de, #339bb9);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#5bc0de), to(#339bb9));background-image:-webkit-linear-gradient(top, #5bc0de, #339bb9);background-image:-o-linear-gradient(top, #5bc0de, #339bb9);background-image:linear-gradient(to bottom, #5bc0de, #339bb9);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff339bb9', GradientType=0)}
.progress-info.progress-striped .bar,.progress-striped .bar-info{background-color:#5bc0de;background-image:-webkit-gradient(linear, 0 100%, 100% 0, color-stop(.25, rgba(255,255,255,0.15)), color-stop(.25, transparent), color-stop(.5, transparent), color-stop(.5, rgba(255,255,255,0.15)), color-stop(.75, rgba(255,255,255,0.15)), color-stop(.75, transparent), to(transparent));background-image:-webkit-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-moz-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-o-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent)}
.progress-warning .bar,.progress .bar-warning{background-color:#faa732;background-image:-moz-linear-gradient(top, #fbb450, #f89406);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#fbb450), to(#f89406));background-image:-webkit-linear-gradient(top, #fbb450, #f89406);background-image:-o-linear-gradient(top, #fbb450, #f89406);background-image:linear-gradient(to bottom, #fbb450, #f89406);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffbb450', endColorstr='#fff89406', GradientType=0)}
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.carousel-caption h4,.carousel-caption p{color:#fff;line-height:20px}
.carousel-caption h4{margin:0 0 5px}
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.hero-unit li{line-height:30px}
.pull-right{float:right}
.pull-left{float:left}
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.show{display:block}
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.affix{position:fixed}
@-ms-viewport{width:device-width}.hidden{display:none;visibility:hidden}
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.visible-tablet{display:none !important}
.hidden-desktop{display:none !important}
.visible-desktop{display:inherit !important}
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<h1 id="implementing-a-simple-perceptron-in-python-theano-version">Implementing a simple perceptron in python - theano version</h1>
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<div class="highlight"><pre><span class="kn">import</span> <span class="nn">pylab</span> <span class="kn">as</span> <span class="nn">pl</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">matplotlib.cm</span> <span class="kn">as</span> <span class="nn">cm</span>
<span class="kn">import</span> <span class="nn">sklearn.cross_validation</span> <span class="kn">as</span> <span class="nn">skcross</span>
<span class="o">%</span><span class="k">matplotlib</span> <span class="n">inline</span>

<span class="n">np</span><span class="o">.</span><span class="n">set_printoptions</span><span class="p">(</span><span class="n">precision</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">suppress</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
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<h2 id="very-quick-introduction-to-theano">Very quick introduction to theano</h2>
<p><a href="http://www.deeplearning.net/software/theano/">Theano</a> is basically a code generator that compiles mathematical expressions into computer code.</p>
<p>The basic idea to keep in mind is that when using Theano, you work with <em>symbolic</em> variables, like you do when you write mathematical expressions. It is only when you call <em>theano.function</em> that code
(GPU code if you target the GPU, numpy code if you target the CPU) gets generated to compute your function.</p>
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<div class="highlight"><pre><span class="c"># Note that this require a kernel restart to be effective</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s">&#39;THEANO_FLAGS&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s">&#39;device=cpu&#39;</span>
<span class="c">#os.environ[&#39;THEANO_FLAGS&#39;] = &#39;device=gpu&#39;</span>
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<div class="highlight"><pre><span class="kn">import</span> <span class="nn">theano.tensor</span> <span class="kn">as</span> <span class="nn">T</span>
<span class="kn">import</span> <span class="nn">theano</span>
<span class="kn">import</span> <span class="nn">tempfile</span>
<span class="kn">import</span> <span class="nn">pydot</span>
<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">SVG</span>
<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">display</span>

<span class="k">print</span> <span class="s">&#39;device : &#39;</span><span class="p">,</span> <span class="n">theano</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">device</span>
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device :  cpu

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<div class="highlight"><pre><span class="k">def</span> <span class="nf">show_computation_graph</span><span class="p">(</span><span class="n">fn</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Wrapper around theano.printing.pydotprint that show the computation graph</span>
<span class="sd">    for a given theano function</span>
<span class="sd">    Args:</span>
<span class="sd">        fn: the function for which to show the computation graph</span>
<span class="sd">        </span>
<span class="sd">    This requires pydot and graphviz</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">display</span><span class="p">(</span><span class="n">SVG</span><span class="p">(</span><span class="n">theano</span><span class="o">.</span><span class="n">printing</span><span class="o">.</span><span class="n">pydotprint</span><span class="p">(</span><span class="n">fn</span><span class="p">,</span> <span class="n">return_image</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">format</span><span class="o">=</span><span class="s">&#39;svg&#39;</span><span class="p">)))</span>
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<p>We will start with simple theano expressions</p>
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<h3 id="a-x-b">a*x + b</h3>
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<div class="highlight"><pre><span class="n">a</span> <span class="o">=</span> <span class="n">T</span><span class="o">.</span><span class="n">scalar</span><span class="p">(</span><span class="s">&#39;a&#39;</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s">&#39;float32&#39;</span><span class="p">)</span>
<span class="n">b</span> <span class="o">=</span> <span class="n">T</span><span class="o">.</span><span class="n">scalar</span><span class="p">(</span><span class="s">&#39;b&#39;</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s">&#39;float32&#39;</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">T</span><span class="o">.</span><span class="n">scalar</span><span class="p">(</span><span class="s">&#39;x&#39;</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s">&#39;float32&#39;</span><span class="p">)</span>

<span class="n">res</span> <span class="o">=</span> <span class="n">a</span><span class="o">*</span><span class="n">x</span> <span class="o">+</span> <span class="n">b</span>

<span class="c"># compile the function for our target device (GPU or CPU)</span>
<span class="n">res_f</span> <span class="o">=</span> <span class="n">theano</span><span class="o">.</span><span class="n">function</span><span class="p">(</span><span class="n">inputs</span><span class="o">=</span><span class="p">[</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">x</span><span class="p">],</span> <span class="n">outputs</span><span class="o">=</span><span class="n">res</span><span class="p">)</span>

<span class="k">print</span> <span class="n">res_f</span><span class="p">(</span><span class="n">a</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">b</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>

<span class="n">show_computation_graph</span><span class="p">(</span><span class="n">res_f</span><span class="p">)</span>
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13.0

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<h3 id="sigmoid-x-">sigmoid(x)</h3>
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<div class="highlight"><pre><span class="n">x</span> <span class="o">=</span> <span class="n">T</span><span class="o">.</span><span class="n">scalar</span><span class="p">(</span><span class="s">&#39;x&#39;</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s">&#39;float32&#39;</span><span class="p">)</span>
<span class="n">sigmoid_x</span> <span class="o">=</span> <span class="n">T</span><span class="o">.</span><span class="n">nnet</span><span class="o">.</span><span class="n">sigmoid</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">f_x</span> <span class="o">=</span> <span class="n">theano</span><span class="o">.</span><span class="n">function</span><span class="p">(</span><span class="n">inputs</span><span class="o">=</span><span class="p">[</span><span class="n">x</span><span class="p">],</span> <span class="n">outputs</span><span class="o">=</span><span class="n">sigmoid_x</span><span class="p">)</span>
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<div class="highlight"><pre><span class="n">f_x</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
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array(0.7310585975646973, dtype=float32)
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<div class="highlight"><pre><span class="n">xvals</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mi">6</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="n">num</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
<span class="n">yvals</span> <span class="o">=</span> <span class="p">[</span><span class="n">f_x</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">xvals</span><span class="p">]</span>
<span class="n">pl</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">xvals</span><span class="p">,</span> <span class="n">yvals</span><span class="p">)</span>
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[&lt;matplotlib.lines.Line2D at 0x105ec4d10&gt;]
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<div class="prompt input_prompt">In&nbsp;[9]:</div>
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<div class="highlight"><pre><span class="n">show_computation_graph</span><span class="p">(</span><span class="n">f_x</span><span class="p">)</span>
</pre></div>

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<title>G</title>
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<!-- sigmoid -->
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<h4 id="same-thing-but-x-is-a-vector">Same thing but x is a vector</h4>
<p>Using a vector for x lets us compute the sigmoid of a vector of values instead of doing it
one-by-one</p>
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<div class="prompt input_prompt">In&nbsp;[10]:</div>
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<div class="highlight"><pre><span class="n">x</span> <span class="o">=</span> <span class="n">T</span><span class="o">.</span><span class="n">vector</span><span class="p">(</span><span class="s">&#39;x&#39;</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s">&#39;float32&#39;</span><span class="p">)</span>
<span class="n">sigmoid_x</span> <span class="o">=</span> <span class="n">T</span><span class="o">.</span><span class="n">nnet</span><span class="o">.</span><span class="n">sigmoid</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">f_x</span> <span class="o">=</span> <span class="n">theano</span><span class="o">.</span><span class="n">function</span><span class="p">(</span><span class="n">inputs</span><span class="o">=</span><span class="p">[</span><span class="n">x</span><span class="p">],</span> <span class="n">outputs</span><span class="o">=</span><span class="n">sigmoid_x</span><span class="p">)</span>
<span class="c"># without a for</span>
<span class="n">yvals</span> <span class="o">=</span> <span class="n">f_x</span><span class="p">(</span><span class="n">xvals</span><span class="p">)</span>
<span class="n">pl</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">xvals</span><span class="p">,</span> <span class="n">yvals</span><span class="p">)</span>
</pre></div>

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<pre>
[&lt;matplotlib.lines.Line2D at 0x108b73090&gt;]
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<div class="prompt input_prompt">In&nbsp;[11]:</div>
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<div class="highlight"><pre><span class="n">show_computation_graph</span><span class="p">(</span><span class="n">f_x</span><span class="p">)</span>
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<h2 id="perceptron-implementation">Perceptron implementation</h2>
<p>We build a perceptron with a sigmoid activation function. That is</p>
<p>$$o_i = sigmoid(W*x_i + b)$$</p>
<p>Where $sigmoid(x) = \frac{1}{1 + e^{-x}}$</p>
<p>The error is simply ($y_i$ being the expected output value)</p>
<p>$$e_i = (y_i - o_i)^2$$</p>
<p>We train the network with minibatch SGD. That is, at each epoch, we compute the average gradient over a bunch of examples as opposed to a single one.</p>
<p>$$W^{t+1} = W^t - \eta \frac{\partial e_i}{\partial W}$$
$$b^{t+1} = b^t - \eta \frac{\partial e_i}{\partial b}$$</p>
<p>Where $\eta$ is the learning rate.</p>
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<h3 id="error-function-derivative">Error function derivative</h3>
<p>Note that if we add an entry set to 1 at the end of the x vector, we can transform our $Wx + b$ into $W\hat{x} + b$ where $\hat{x} = \left[\begin{matrix}x \\ 1 \end{matrix}\right]$</p>
<p>$$l_i = W\hat{x}$$</p>
<p>$$o_i = sigmoid(l_i)$$</p>
<p>$$e_i = (y_i - o_i)^2$$</p>
<p>We want to compute the derivative of $e_i$ w.r.t $W$. From the <a href="http://en.wikipedia.org/wiki/Chain_rule">chain rule</a>, we have</p>
<p>$$\frac{\partial e_i}{\partial W} = \frac{\partial e_i}{\partial o_i} \frac{\partial o_i}{\partial l_i} \frac{\partial l_i}{\partial W}$$</p>
<p>With
$$\frac{\partial e_i}{\partial o_i} = -2(y_i - o_i)$$
$$\frac{\partial o_i}{\partial l_i} = sigmoid(l_i)(1 - sigmoid(l_i))$$
$$\frac{\partial l_i}{\partial W} = \hat{x}$$</p>
<p>Which gives us</p>
<p>$$\frac{\partial e_i}{\partial W} = -2(y_i - o_i) sigmoid(l_i)(1 - sigmoid(l_i)) \hat{x}$$</p>
<p>Which we use to update our weights at each step of gradient descent :</p>
<p>$$W^{t+1} = W^t - \frac{\partial e_i}{\partial W}$$</p>
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<div class="highlight"><pre><span class="c"># number of features</span>
<span class="n">X_dim</span> <span class="o">=</span> <span class="mi">2</span>

<span class="c"># weights</span>
<span class="n">W_init</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">(</span><span class="n">X_dim</span><span class="p">)</span> <span class="o">-</span> <span class="mf">0.5</span><span class="p">)</span>
<span class="n">W</span> <span class="o">=</span> <span class="n">theano</span><span class="o">.</span><span class="n">shared</span><span class="p">(</span>
    <span class="n">value</span><span class="o">=</span><span class="n">W_init</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span>
    <span class="n">name</span><span class="o">=</span><span class="s">&#39;W&#39;</span><span class="p">,</span>
    <span class="n">borrow</span><span class="o">=</span><span class="bp">True</span>
<span class="p">)</span>

<span class="c"># bias</span>
<span class="n">b_init</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">()</span> <span class="o">-</span> <span class="mf">0.5</span><span class="p">)</span>
<span class="n">b</span> <span class="o">=</span> <span class="n">theano</span><span class="o">.</span><span class="n">shared</span><span class="p">(</span>
    <span class="n">value</span> <span class="o">=</span> <span class="n">b_init</span><span class="p">,</span>
    <span class="n">name</span><span class="o">=</span><span class="s">&#39;b&#39;</span><span class="p">,</span>
<span class="p">)</span>

<span class="c"># data matrix</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">T</span><span class="o">.</span><span class="n">matrix</span><span class="p">(</span><span class="s">&#39;X&#39;</span><span class="p">)</span>
<span class="c"># labels vector</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">T</span><span class="o">.</span><span class="n">vector</span><span class="p">(</span><span class="s">&#39;y&#39;</span><span class="p">)</span>

<span class="n">y_pred</span> <span class="o">=</span> <span class="n">T</span><span class="o">.</span><span class="n">nnet</span><span class="o">.</span><span class="n">sigmoid</span><span class="p">(</span><span class="n">T</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">W</span><span class="p">,</span> <span class="n">X</span><span class="o">.</span><span class="n">T</span><span class="p">)</span> <span class="o">+</span> <span class="n">b</span><span class="p">)</span>

<span class="n">error</span> <span class="o">=</span> <span class="n">T</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">T</span><span class="o">.</span><span class="n">sqr</span><span class="p">(</span><span class="n">y</span> <span class="o">-</span> <span class="n">y_pred</span><span class="p">))</span>

<span class="c"># compilation</span>
<span class="n">y_pred_fn</span> <span class="o">=</span> <span class="n">theano</span><span class="o">.</span><span class="n">function</span><span class="p">(</span>
    <span class="n">inputs</span><span class="o">=</span><span class="p">[</span><span class="n">X</span><span class="p">],</span>
    <span class="n">outputs</span><span class="o">=</span><span class="n">y_pred</span>
<span class="p">)</span>

<span class="n">error_fn</span> <span class="o">=</span> <span class="n">theano</span><span class="o">.</span><span class="n">function</span><span class="p">(</span>
    <span class="n">inputs</span><span class="o">=</span><span class="p">[</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">],</span>
    <span class="n">outputs</span><span class="o">=</span><span class="n">error</span>
<span class="p">)</span>
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<div class="highlight"><pre><span class="c"># You can explore the computation graph as follow :</span>
<span class="k">if</span> <span class="bp">False</span><span class="p">:</span>
    <span class="n">show_computation_graph</span><span class="p">(</span><span class="n">error</span><span class="p">)</span>
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<div class="highlight"><pre><span class="c"># A forward pass through our perceptron</span>
<span class="n">error_fn</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]],</span> <span class="n">y</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
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array(1.4242917014172882)
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<div class="highlight"><pre><span class="c"># For backprop, we need the gradient with regard to our weights and bias</span>
<span class="n">g_W</span> <span class="o">=</span> <span class="n">T</span><span class="o">.</span><span class="n">grad</span><span class="p">(</span><span class="n">cost</span><span class="o">=</span><span class="n">error</span><span class="p">,</span> <span class="n">wrt</span><span class="o">=</span><span class="n">W</span><span class="p">)</span>
<span class="n">g_b</span> <span class="o">=</span> <span class="n">T</span><span class="o">.</span><span class="n">grad</span><span class="p">(</span><span class="n">cost</span><span class="o">=</span><span class="n">error</span><span class="p">,</span> <span class="n">wrt</span><span class="o">=</span><span class="n">b</span><span class="p">)</span>
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<div class="highlight"><pre><span class="c"># We can compute the gradient wrt the weights as follow</span>
<span class="n">g_W_fn</span> <span class="o">=</span> <span class="n">theano</span><span class="o">.</span><span class="n">function</span><span class="p">(</span>
    <span class="n">inputs</span><span class="o">=</span><span class="p">[</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">],</span>
    <span class="n">outputs</span><span class="o">=</span><span class="n">g_W</span>
<span class="p">)</span>

<span class="c"># Gradient for X=[0, 1] and y=0</span>
<span class="n">g_W_fn</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]],</span> <span class="n">y</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
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array([[ 0.     ,  0.07662]])
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<div class="highlight"><pre><span class="c"># same for bias</span>
<span class="n">g_b_fn</span> <span class="o">=</span> <span class="n">theano</span><span class="o">.</span><span class="n">function</span><span class="p">(</span>
    <span class="n">inputs</span><span class="o">=</span><span class="p">[</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">],</span>
    <span class="n">outputs</span><span class="o">=</span><span class="n">g_b</span>
<span class="p">)</span>
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<p>We can check that theano&#39;s computed gradient match the derivation we made by hand.</p>
<p>Note that this seems to run into numerical accuracy issues.</p>
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<div class="highlight"><pre><span class="k">def</span> <span class="nf">sigmoid</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
    <span class="k">return</span> <span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">x</span><span class="p">))</span>

<span class="k">def</span> <span class="nf">my_grad</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">W</span><span class="p">,</span> <span class="n">b</span><span class="p">):</span>
    <span class="n">o</span> <span class="o">=</span> <span class="n">sigmoid</span><span class="p">(</span><span class="n">W</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="o">+</span> <span class="n">b</span><span class="p">)</span>
    <span class="c"># The last X is the derivative of o wrt X, which is X</span>
    <span class="n">gW</span> <span class="o">=</span> <span class="o">-</span><span class="mi">2</span><span class="o">*</span><span class="p">(</span><span class="n">y</span> <span class="o">-</span> <span class="n">o</span><span class="p">)</span><span class="o">*</span><span class="n">sigmoid</span><span class="p">(</span><span class="n">o</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">sigmoid</span><span class="p">(</span><span class="n">o</span><span class="p">))</span><span class="o">*</span><span class="n">X</span>
    <span class="c"># The last 1 is the derivative of o wrt b, which is 1</span>
    <span class="n">gb</span> <span class="o">=</span> <span class="o">-</span><span class="mi">2</span><span class="o">*</span><span class="p">(</span><span class="n">y</span> <span class="o">-</span> <span class="n">o</span><span class="p">)</span><span class="o">*</span><span class="n">sigmoid</span><span class="p">(</span><span class="n">o</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">sigmoid</span><span class="p">(</span><span class="n">o</span><span class="p">))</span><span class="o">*</span><span class="mi">1</span>
    
    <span class="k">return</span> <span class="n">gW</span><span class="p">,</span> <span class="n">gb</span>

<span class="n">X1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
<span class="n">y1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
<span class="k">print</span> <span class="n">my_grad</span><span class="p">(</span><span class="n">X1</span><span class="p">,</span> <span class="n">y1</span><span class="p">,</span> <span class="n">W</span><span class="o">.</span><span class="n">get_value</span><span class="p">(),</span> <span class="n">b</span><span class="o">.</span><span class="n">get_value</span><span class="p">())</span>
<span class="k">print</span> <span class="n">g_W_fn</span><span class="p">([</span><span class="n">X1</span><span class="p">],</span> <span class="n">y1</span><span class="p">),</span> <span class="n">g_b_fn</span><span class="p">([</span><span class="n">X1</span><span class="p">],</span> <span class="n">y1</span><span class="p">)</span>

<span class="k">print</span> <span class="s">&#39;--&#39;</span>
<span class="n">X1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="o">-</span><span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
<span class="n">y1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.5</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
<span class="k">print</span> <span class="n">my_grad</span><span class="p">(</span><span class="n">X1</span><span class="p">,</span> <span class="n">y1</span><span class="p">,</span> <span class="n">W</span><span class="o">.</span><span class="n">get_value</span><span class="p">(),</span> <span class="n">b</span><span class="o">.</span><span class="n">get_value</span><span class="p">())</span>
<span class="k">print</span> <span class="n">g_W_fn</span><span class="p">([</span><span class="n">X1</span><span class="p">],</span> <span class="n">y1</span><span class="p">),</span> <span class="n">g_b_fn</span><span class="p">([</span><span class="n">X1</span><span class="p">],</span> <span class="n">y1</span><span class="p">)</span>
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(array([ 0.     ,  0.10959]), array([ 0.10959]))
[[ 0.       0.07662]] 0.0766240492623
--
(array([ 0.02558, -0.08954]), array([-0.12791]))
[[ 0.01896 -0.06636]] -0.0948039833346

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<p>We could use g_W_fn to loop over our samples and update our weights. But theano has a nice <em>updates</em> parameters that you can give to the <em>theano.function</em>. It specifies which variables should be updated and how.</p>
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<div class="highlight"><pre><span class="n">learning_rate</span> <span class="o">=</span> <span class="mi">1</span>
<span class="n">updates</span> <span class="o">=</span> <span class="p">[</span>
    <span class="p">(</span><span class="n">W</span><span class="p">,</span> <span class="n">W</span> <span class="o">-</span> <span class="n">learning_rate</span> <span class="o">*</span> <span class="n">g_W</span><span class="p">),</span>
    <span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">b</span> <span class="o">-</span> <span class="n">learning_rate</span> <span class="o">*</span> <span class="n">g_b</span><span class="p">)</span>
<span class="p">]</span>

<span class="n">train_model</span> <span class="o">=</span> <span class="n">theano</span><span class="o">.</span><span class="n">function</span><span class="p">(</span>
    <span class="n">inputs</span><span class="o">=</span><span class="p">[</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">],</span>
    <span class="n">outputs</span><span class="o">=</span><span class="n">error</span><span class="p">,</span>
    <span class="n">updates</span><span class="o">=</span><span class="n">updates</span>
<span class="p">)</span>

<span class="n">predict</span> <span class="o">=</span> <span class="n">theano</span><span class="o">.</span><span class="n">function</span><span class="p">(</span>
    <span class="n">inputs</span><span class="o">=</span><span class="p">[</span><span class="n">X</span><span class="p">],</span>
    <span class="n">outputs</span><span class="o">=</span><span class="n">y_pred</span>
<span class="p">)</span>
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<div class="highlight"><pre><span class="n">W</span><span class="o">.</span><span class="n">set_value</span><span class="p">([[</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">]])</span>
<span class="n">b</span><span class="o">.</span><span class="n">set_value</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>

<span class="n">X1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
<span class="n">y1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
<span class="k">print</span> <span class="s">&quot;prediction        &quot;</span><span class="p">,</span> <span class="n">predict</span><span class="p">(</span><span class="n">X1</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">xrange</span><span class="p">(</span><span class="mi">5</span><span class="p">):</span>
    <span class="k">print</span> <span class="s">&#39;---- iteration&#39;</span><span class="p">,</span> <span class="n">i</span>
    <span class="k">print</span> <span class="s">&quot;W, b before       &quot;</span><span class="p">,</span> <span class="n">W</span><span class="o">.</span><span class="n">get_value</span><span class="p">(),</span> <span class="n">b</span><span class="o">.</span><span class="n">get_value</span><span class="p">()</span>
    <span class="k">print</span> <span class="s">&quot;gradient wrt W, b &quot;</span><span class="p">,</span> <span class="n">g_W_fn</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="n">X1</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">y1</span><span class="p">),</span> <span class="n">g_b_fn</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="n">X1</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">y1</span><span class="p">)</span>
    <span class="n">train_model</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="n">X1</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">y1</span><span class="p">)</span>
    <span class="k">print</span> <span class="s">&quot;W, b after updates&quot;</span><span class="p">,</span> <span class="n">W</span><span class="o">.</span><span class="n">get_value</span><span class="p">(),</span> <span class="n">b</span><span class="o">.</span><span class="n">get_value</span><span class="p">()</span>
    <span class="k">print</span> <span class="s">&quot;prediction        &quot;</span><span class="p">,</span> <span class="n">predict</span><span class="p">(</span><span class="n">X1</span><span class="p">)</span>
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prediction         [[ 0.62246]]
---- iteration 0
W, b before        [[ 0.5  0.5]] 0.0
gradient wrt W, b  [[ 0.       0.29256]] 0.292560507054
W, b after updates [[ 0.5      0.20744]] -0.292560507054
prediction         [[ 0.47873]]
---- iteration 1
W, b before        [[ 0.5      0.20744]] -0.292560507054
gradient wrt W, b  [[ 0.       0.23893]] 0.238933228816
W, b after updates [[ 0.5     -0.03149]] -0.53149373587
prediction         [[ 0.36286]]
---- iteration 2
W, b before        [[ 0.5     -0.03149]] -0.53149373587
gradient wrt W, b  [[ 0.       0.16778]] 0.167778792514
W, b after updates [[ 0.5     -0.19927]] -0.699272528385
prediction         [[ 0.28935]]
---- iteration 3
W, b before        [[ 0.5     -0.19927]] -0.699272528385
gradient wrt W, b  [[ 0.     0.119]] 0.118995825535
W, b after updates [[ 0.5     -0.31827]] -0.818268353919
prediction         [[ 0.24296]]
---- iteration 4
W, b before        [[ 0.5     -0.31827]] -0.818268353919
gradient wrt W, b  [[ 0.       0.08937]] 0.0893734977481
W, b after updates [[ 0.5     -0.40764]] -0.907641851668
prediction         [[ 0.2116]]

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<h3 id="important-note">Important note</h3>
<p>Note that this is a naive implementation and it will suffer from one major problem if ran onto the GPU : the data (X) will be uploaded to the GPU on every call to our functions. This will most likely using the GPU will be slower than a CPU for this particular implementation.</p>
<p>Of course, Theano allows you to avoid this and upload your data once to the GPU. The <a href="http://deeplearning.net/tutorial/logreg.html">logistic regression theano tutorial</a> is a good reference for how to do minibatch training with the data in GPU memory. The key is to upload X and y once and then simply provide the index of the sample as an argument to our various functions.</p>
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<h3 id="let-s-put-everything-in-a-class">Let&#39;s put everything in a class</h3>
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<p>This is a perceptron class that contains the same code as above, for ease-of-reuse</p>
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<div class="highlight"><pre><span class="k">class</span> <span class="nc">Perceptron</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X_dim</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.2</span><span class="p">):</span>
        <span class="c"># theano variable definitions</span>
        <span class="n">W_init</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">(</span><span class="n">X_dim</span><span class="p">))</span> <span class="o">*</span> <span class="mf">0.25</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">W</span> <span class="o">=</span> <span class="n">theano</span><span class="o">.</span><span class="n">shared</span><span class="p">(</span>
            <span class="n">value</span><span class="o">=</span><span class="n">W_init</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span>
            <span class="n">name</span><span class="o">=</span><span class="s">&#39;W&#39;</span><span class="p">,</span>
            <span class="n">borrow</span><span class="o">=</span><span class="bp">True</span>
        <span class="p">)</span>
        <span class="n">b_init</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">())</span> <span class="o">*</span> <span class="mf">0.25</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">b</span> <span class="o">=</span> <span class="n">theano</span><span class="o">.</span><span class="n">shared</span><span class="p">(</span>
            <span class="n">value</span> <span class="o">=</span> <span class="n">b_init</span><span class="p">,</span>
            <span class="n">name</span><span class="o">=</span><span class="s">&#39;b&#39;</span><span class="p">,</span>
        <span class="p">)</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">X</span> <span class="o">=</span> <span class="n">T</span><span class="o">.</span><span class="n">matrix</span><span class="p">(</span><span class="s">&#39;X&#39;</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">y</span> <span class="o">=</span> <span class="n">T</span><span class="o">.</span><span class="n">vector</span><span class="p">(</span><span class="s">&#39;y&#39;</span><span class="p">)</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">y_pred</span> <span class="o">=</span> <span class="n">T</span><span class="o">.</span><span class="n">nnet</span><span class="o">.</span><span class="n">sigmoid</span><span class="p">(</span><span class="n">T</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">W</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">X</span><span class="o">.</span><span class="n">T</span><span class="p">)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">b</span><span class="p">)</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">error</span> <span class="o">=</span> <span class="n">T</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">T</span><span class="o">.</span><span class="n">sqr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">y</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">y_pred</span><span class="p">))</span>
        
        <span class="bp">self</span><span class="o">.</span><span class="n">g_W</span> <span class="o">=</span> <span class="n">T</span><span class="o">.</span><span class="n">grad</span><span class="p">(</span><span class="n">cost</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">error</span><span class="p">,</span> <span class="n">wrt</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">W</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">g_b</span> <span class="o">=</span> <span class="n">T</span><span class="o">.</span><span class="n">grad</span><span class="p">(</span><span class="n">cost</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">error</span><span class="p">,</span> <span class="n">wrt</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">b</span><span class="p">)</span>
        
        <span class="c"># theano functions</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">predict_fn</span> <span class="o">=</span> <span class="n">theano</span><span class="o">.</span><span class="n">function</span><span class="p">(</span>
            <span class="n">inputs</span><span class="o">=</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">X</span><span class="p">],</span>
            <span class="n">outputs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">y_pred</span>
        <span class="p">)</span>
        
        <span class="bp">self</span><span class="o">.</span><span class="n">error_fn</span> <span class="o">=</span> <span class="n">theano</span><span class="o">.</span><span class="n">function</span><span class="p">(</span>
            <span class="n">inputs</span><span class="o">=</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">X</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">y</span><span class="p">],</span>
            <span class="n">outputs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">error</span>
        <span class="p">)</span>
        
        <span class="n">updates</span> <span class="o">=</span> <span class="p">[</span>
            <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">W</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">W</span> <span class="o">-</span> <span class="n">learning_rate</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">g_W</span><span class="p">),</span>
            <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">b</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">b</span> <span class="o">-</span> <span class="n">learning_rate</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">g_b</span><span class="p">)</span>
        <span class="p">]</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">train_model_fn</span> <span class="o">=</span> <span class="n">theano</span><span class="o">.</span><span class="n">function</span><span class="p">(</span>
            <span class="n">inputs</span><span class="o">=</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">X</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">y</span><span class="p">],</span>
            <span class="n">outputs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">error</span><span class="p">,</span>
            <span class="n">updates</span><span class="o">=</span><span class="n">updates</span>
        <span class="p">)</span>
        
        <span class="c"># non-theano stuff</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">train_errors</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">validation_errors</span> <span class="o">=</span> <span class="p">[]</span>
        
    <span class="k">def</span> <span class="nf">train_gd</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_validation</span><span class="p">,</span> <span class="n">y_validation</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">10</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;Simple gradient descent training&quot;&quot;&quot;</span>
        <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">X_train</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span><span class="p">,</span> <span class="s">&quot;X must be 2D&quot;</span>
        
        <span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">epochs</span><span class="p">):</span>
            <span class="c"># This does forward propagation, gradient computation and weights</span>
            <span class="c"># update all in one</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">train_model_fn</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
            
            <span class="c"># evaluate train/validation errors</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">train_errors</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">error_fn</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
            <span class="p">)</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">validation_errors</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">error_fn</span><span class="p">(</span><span class="n">X_validation</span><span class="p">,</span> <span class="n">y_validation</span><span class="p">)</span>
            <span class="p">)</span>
        
        <span class="k">return</span> <span class="bp">self</span>
                    
    <span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;Binary (0, 1) prediction&quot;&quot;&quot;</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">predict_fn</span><span class="p">(</span><span class="n">X</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">int</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">decision_function</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;Float [0,1] prediction&quot;&quot;&quot;</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">predict_fn</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
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<h2 id="generate-a-toy-dataset-and-test">Generate a toy dataset and test</h2>
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<div class="highlight"><pre><span class="k">def</span> <span class="nf">dataset_fixed_cov</span><span class="p">():</span>
    <span class="sd">&quot;&quot;&quot;Generate 2 Gaussians samples with the same covariance matrix&quot;&quot;&quot;</span>
    <span class="n">n</span><span class="p">,</span> <span class="n">dim</span> <span class="o">=</span> <span class="mi">300</span><span class="p">,</span> <span class="mi">2</span>
    <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
    <span class="n">C</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.23</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.83</span><span class="p">,</span> <span class="o">.</span><span class="mi">23</span><span class="p">]])</span>
    <span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">dim</span><span class="p">),</span> <span class="n">C</span><span class="p">),</span>
              <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">dim</span><span class="p">),</span> <span class="n">C</span><span class="p">)</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">])]</span>
    <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">((</span><span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">n</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">n</span><span class="p">)))</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">int</span><span class="p">)</span>
    <span class="n">X</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
    <span class="n">y</span> <span class="o">=</span> <span class="n">y</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span>

<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">dataset_fixed_cov</span><span class="p">()</span>

<span class="k">print</span> <span class="n">X</span>
</pre></div>

</div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area"><div class="prompt"></div>
<div class="output_subarea output_stream output_stdout output_text">
<pre>
[[-0.11476 -0.14605]
 [ 1.26411  0.20133]
 [-0.19433  0.     ]
 ..., 
 [ 1.36559  1.4677 ]
 [ 1.45857  1.13159]
 [ 2.13224  1.26225]]

</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[23]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class="highlight"><pre><span class="n">pl</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="n">y</span><span class="o">==</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[</span><span class="n">y</span><span class="o">==</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="s">&#39;r&#39;</span><span class="p">)</span>
<span class="n">pl</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="n">y</span><span class="o">==</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[</span><span class="n">y</span><span class="o">==</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="s">&#39;b&#39;</span><span class="p">)</span>
</pre></div>

</div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area"><div class="prompt output_prompt">Out[23]:</div>


<div class="output_text output_subarea output_pyout">
<pre>
&lt;matplotlib.collections.PathCollection at 0x1093fbfd0&gt;
</pre>
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</div>

<div class="output_area"><div class="prompt"></div>


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"
>
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<div class="prompt input_prompt">In&nbsp;[24]:</div>
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<div class="highlight"><pre><span class="n">X_train</span><span class="p">,</span> <span class="n">X_validation</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_validation</span> <span class="o">=</span> <span class="n">skcross</span><span class="o">.</span><span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.33</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
<span class="n">perceptron</span> <span class="o">=</span> <span class="n">Perceptron</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.2</span><span class="p">)</span>
<span class="n">perceptron</span><span class="o">.</span><span class="n">train_gd</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_validation</span><span class="p">,</span> <span class="n">y_validation</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">800</span><span class="p">)</span>
<span class="c">#perceptron.train_minisgd(X_train, y_train, X_validation, y_validation, epochs=500, minibatch_size=10)</span>
</pre></div>

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<pre>
&lt;__main__.Perceptron at 0x105e16b50&gt;
</pre>
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<div class="prompt input_prompt">In&nbsp;[25]:</div>
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<div class="highlight"><pre><span class="n">pl</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">perceptron</span><span class="o">.</span><span class="n">train_errors</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">&#39;train&#39;</span><span class="p">)</span>
<span class="n">pl</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">perceptron</span><span class="o">.</span><span class="n">validation_errors</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">&#39;validation&#39;</span><span class="p">)</span>
<span class="n">pl</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
</pre></div>

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<div class="output_area"><div class="prompt output_prompt">Out[25]:</div>


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<pre>
&lt;matplotlib.legend.Legend at 0x109884cd0&gt;
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<div class="prompt input_prompt">In&nbsp;[26]:</div>
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    <div class="input_area">
<div class="highlight"><pre><span class="kn">from</span> <span class="nn">mpl_toolkits.axes_grid1</span> <span class="kn">import</span> <span class="n">make_axes_locatable</span>
<span class="kn">import</span> <span class="nn">matplotlib.gridspec</span> <span class="kn">as</span> <span class="nn">gridspec</span>

<span class="k">def</span> <span class="nf">show_decision_boundary</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">subplot_spec</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Utility function to plot the decision function of a classifier</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">assert</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="mi">2</span>
    <span class="n">wratio</span> <span class="o">=</span> <span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
    <span class="k">if</span> <span class="n">subplot_spec</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
        <span class="n">gs</span> <span class="o">=</span> <span class="n">gridspec</span><span class="o">.</span><span class="n">GridSpec</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span> <span class="n">width_ratios</span><span class="o">=</span><span class="n">wratio</span><span class="p">)</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="n">gs</span> <span class="o">=</span> <span class="n">gridspec</span><span class="o">.</span><span class="n">GridSpecFromSubplotSpec</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">subplot_spec</span><span class="o">=</span><span class="n">subplot_spec</span><span class="p">,</span> <span class="n">width_ratios</span><span class="o">=</span><span class="n">wratio</span><span class="p">)</span>
        
    <span class="n">ax</span> <span class="o">=</span> <span class="n">pl</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="n">gs</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
    <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s">&#39;Dataset and decision function&#39;</span><span class="p">)</span>
    
    <span class="n">x_min</span><span class="p">,</span> <span class="n">x_max</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">X</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">+</span> <span class="mi">1</span>
    <span class="n">y_min</span><span class="p">,</span> <span class="n">y_max</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">X</span><span class="p">[:,</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">+</span> <span class="mi">1</span>
    <span class="n">h</span> <span class="o">=</span> <span class="mf">0.2</span> <span class="c"># step size in the meshgrid</span>
    <span class="n">xx</span><span class="p">,</span> <span class="n">yy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">x_min</span><span class="p">,</span> <span class="n">x_max</span><span class="p">,</span> <span class="n">h</span><span class="p">),</span>
                         <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">y_min</span><span class="p">,</span> <span class="n">y_max</span><span class="p">,</span> <span class="n">h</span><span class="p">))</span>
    <span class="n">xx</span> <span class="o">=</span> <span class="n">xx</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
    <span class="n">yy</span> <span class="o">=</span> <span class="n">yy</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>

    <span class="n">Z</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">decision_function</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">c_</span><span class="p">[</span><span class="n">xx</span><span class="o">.</span><span class="n">ravel</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">ravel</span><span class="p">()])</span>
    <span class="n">Z</span> <span class="o">=</span> <span class="n">Z</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">xx</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
    <span class="n">ctr</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">xx</span><span class="p">,</span> <span class="n">yy</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cm</span><span class="o">.</span><span class="n">gray</span><span class="p">,</span> <span class="n">vmin</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
    
    <span class="n">unique_labels</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
    <span class="n">colors</span> <span class="o">=</span> <span class="n">cm</span><span class="o">.</span><span class="n">Paired</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">num</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">unique_labels</span><span class="p">)))</span>
    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">yi</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">unique_labels</span><span class="p">):</span>
        <span class="n">color</span> <span class="o">=</span> <span class="n">colors</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
        <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="n">y</span> <span class="o">==</span> <span class="n">yi</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[</span><span class="n">y</span> <span class="o">==</span> <span class="n">yi</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">color</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">&#39;</span><span class="si">%d</span><span class="s">&#39;</span> <span class="o">%</span> <span class="n">yi</span><span class="p">)</span>
    <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
    <span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">((</span><span class="n">x_min</span><span class="p">,</span> <span class="n">x_max</span><span class="p">))</span>
    <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">((</span><span class="n">y_min</span><span class="p">,</span> <span class="n">y_max</span><span class="p">))</span>

    <span class="n">pl</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">ctr</span><span class="p">,</span> <span class="n">cax</span><span class="o">=</span><span class="n">pl</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="n">gs</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>

<span class="n">show_decision_boundary</span><span class="p">(</span><span class="n">perceptron</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
</pre></div>

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<h2 id="making-a-video-of-the-training">Making a video of the training</h2>
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<div class="prompt input_prompt">In&nbsp;[29]:</div>
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<div class="highlight"><pre><span class="kn">import</span> <span class="nn">matplotlib.animation</span> <span class="kn">as</span> <span class="nn">manimation</span>
<span class="kn">from</span> <span class="nn">tempfile</span> <span class="kn">import</span> <span class="n">NamedTemporaryFile</span>
<span class="kn">from</span> <span class="nn">matplotlib.legend_handler</span> <span class="kn">import</span> <span class="n">HandlerLine2D</span>

<span class="c"># Depending on your environment, you might have to use any of the 2 following</span>
<span class="n">AVConvWriter</span> <span class="o">=</span> <span class="n">manimation</span><span class="o">.</span><span class="n">writers</span><span class="p">[</span><span class="s">&#39;ffmpeg&#39;</span><span class="p">]</span> <span class="c"># or &#39;avconv&#39; if you&#39;re on ubuntu</span>
<span class="n">metadata</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">title</span><span class="o">=</span><span class="s">&#39;Perceptron training&#39;</span><span class="p">,</span> <span class="n">artist</span><span class="o">=</span><span class="s">&#39;&#39;</span><span class="p">,</span><span class="n">comment</span><span class="o">=</span><span class="s">&#39;&#39;</span><span class="p">)</span>
<span class="n">writer</span> <span class="o">=</span> <span class="n">AVConvWriter</span><span class="p">(</span><span class="n">fps</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">metadata</span><span class="o">=</span><span class="n">metadata</span><span class="p">,</span> <span class="n">codec</span><span class="o">=</span><span class="s">&quot;libx264&quot;</span><span class="p">,</span> <span class="n">extra_args</span><span class="o">=</span><span class="p">[</span><span class="s">&#39;-vcodec&#39;</span><span class="p">,</span> <span class="s">&#39;libx264&#39;</span><span class="p">])</span>

<span class="c">#writer = manimation.FFMpegFileWriter(fps=4)</span>

<span class="n">perceptron</span> <span class="o">=</span> <span class="n">Perceptron</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>

<span class="n">fig</span> <span class="o">=</span> <span class="n">pl</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
<span class="n">gs</span> <span class="o">=</span> <span class="n">gridspec</span><span class="o">.</span><span class="n">GridSpec</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">wspace</span><span class="o">=</span><span class="mf">0.4</span><span class="p">)</span>

<span class="n">nepochs</span> <span class="o">=</span> <span class="mi">800</span>

<span class="n">err_ymax</span> <span class="o">=</span> <span class="bp">None</span>

<span class="k">with</span> <span class="n">NamedTemporaryFile</span><span class="p">(</span><span class="n">suffix</span><span class="o">=</span><span class="s">&#39;.mp4&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="k">with</span> <span class="n">writer</span><span class="o">.</span><span class="n">saving</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">f</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="mi">100</span><span class="p">):</span>
        <span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">xrange</span><span class="p">(</span><span class="n">nepochs</span><span class="p">):</span>
            <span class="n">perceptron</span><span class="o">.</span><span class="n">train_gd</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_validation</span><span class="p">,</span> <span class="n">y_validation</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
            <span class="c">#perceptron.train_minisgd(X_train, y_train, X_validation, y_validation, epochs=1, minibatch_size=10)</span>

            <span class="k">if</span> <span class="n">err_ymax</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
                <span class="n">err_ymax</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">perceptron</span><span class="o">.</span><span class="n">train_errors</span><span class="p">),</span>
                               <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">perceptron</span><span class="o">.</span><span class="n">validation_errors</span><span class="p">))</span> <span class="o">*</span> <span class="mf">1.1</span>

            <span class="k">if</span> <span class="n">epoch</span> <span class="o">%</span> <span class="mi">10</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> <span class="c"># Capturing all 800 epochs takes forever...</span>
                <span class="n">show_decision_boundary</span><span class="p">(</span><span class="n">perceptron</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">gs</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>

                <span class="n">ax</span> <span class="o">=</span> <span class="n">pl</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="n">gs</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
                <span class="n">pl</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s">&#39;Error&#39;</span><span class="p">)</span>
                <span class="n">line_train</span><span class="p">,</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">perceptron</span><span class="o">.</span><span class="n">train_errors</span><span class="p">),</span> <span class="n">c</span><span class="o">=</span><span class="s">&#39;b&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">&#39;train&#39;</span><span class="p">)</span>
                <span class="n">line_valid</span><span class="p">,</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">perceptron</span><span class="o">.</span><span class="n">validation_errors</span><span class="p">),</span> <span class="n">c</span><span class="o">=</span><span class="s">&#39;g&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">&#39;validation&#39;</span><span class="p">)</span>
                <span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">nepochs</span><span class="p">)</span>
                <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">err_ymax</span><span class="p">)</span>
                <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s">&#39;epochs&#39;</span><span class="p">)</span>
                <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s">&#39;error&#39;</span><span class="p">)</span>
                <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">((</span><span class="n">line_train</span><span class="p">,</span> <span class="n">line_valid</span><span class="p">),</span> <span class="p">(</span><span class="s">&#39;train&#39;</span><span class="p">,</span> <span class="s">&#39;validation&#39;</span><span class="p">))</span>

                <span class="n">writer</span><span class="o">.</span><span class="n">grab_frame</span><span class="p">()</span>
            
    <span class="n">video</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="n">f</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="s">&quot;rb&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
</pre></div>

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</div>
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"
>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[30]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class="highlight"><pre><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>

<span class="n">VIDEO_TAG</span> <span class="o">=</span> <span class="s">&quot;&quot;&quot;&lt;video style=&quot;width:80%&quot; controls&gt;</span>
<span class="s"> &lt;source src=&quot;data:video/x-m4v;base64,{0}&quot; type=&quot;video/mp4&quot;&gt;</span>
<span class="s"> Your browser does not support the video tag.</span>
<span class="s">&lt;/video&gt;&quot;&quot;&quot;</span>

<span class="n">HTML</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">VIDEO_TAG</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">video</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="s">&quot;base64&quot;</span><span class="p">)))</span>
</pre></div>

</div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area"><div class="prompt output_prompt">Out[30]:</div>

<div class="output_html rendered_html output_subarea output_pyout">
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<h2 id="interesting-links">Interesting links</h2>
<p>Tutorials :</p>
<p><a href="http://deeplearning.net/software/theano/tutorial/">Theano tutorials</a></p>
<p><a href="http://deeplearning.net/tutorial/contents.html">Deep learning tutorials using Theano</a></p>
<p>Deep learning libraries using theano :</p>
<p><a href="https://github.com/Lasagne/Lasagne">Lasagne</a></p>
<p><a href="https://github.com/bartvm/blocks">Blocks</a></p>
<p><a href="http://deeplearning.net/software/pylearn2/">pylearn2</a></p>
<p>General deep learning :</p>
<p><a href="http://deeplearning.net/reading-list/">Reading recommendations</a></p>
<p><a href="http://deeplearning.net/software_links/">Software</a></p>
<p><a href="http://cs231n.stanford.edu/">Stanford course about CNN</a></p>
<p>Cool demos :</p>
<p><a href="http://cs.stanford.edu/people/karpathy/convnetjs/">CNN in your browser</a></p>
<p>Misc :</p>
<p><a href="https://groups.google.com/forum/#!topic/theano-users/o-BLhLvnL5s">How to access theano generated code</a></p>
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<h2 id="additional-stuff">Additional stuff</h2>
<p>To add minibatch SGD training to the perceptron, use the following code. Note that since we do more weight updates per epoch (since we update for each minibatch), this requires less epoch to converge.</p>
<pre><code>def train_minisgd(self, X_train, y_train, X_validation, y_validation, epochs=10, minibatch_size=10):
    &quot;&quot;&quot;Training with minibatch SGD&quot;&quot;&quot;
    assert len(X_train.shape) == 2, &quot;X must be 2D&quot;

    for epoch in range(epochs):
        minibatch_indices = np.arange(X_train.shape[0])
        np.random.shuffle(minibatch_indices)
        # for each minibatch, compute gradient of weights
        for start in xrange(0, len(minibatch_indices), minibatch_size):
            end = start + minibatch_size
            indices = minibatch_indices[start:end]
            Xb = X_train[indices]
            yb = y_train[indices]

            self.train_model_fn(Xb, yb)

        # evaluate train/validation errors
        self.train_errors.append(
            self.error_fn(X_train, y_train)
        )
        self.validation_errors.append(
            self.error_fn(X_validation, y_validation)
        )

    return self
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